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Additive Manufacturing of Nickel components using CMT

process

Inês Planas Meunier Santos Pinto

Thesis to obtain the Master of Science Degree in

Mechanical Engineering

Supervisors: Prof. Eurico Gonçalves Assunção

Prof. Maria Luísa Coutinho Gomes de Almeida

Examination Committee

Chairperson: Prof. Rui Manuel dos Santos Oliveira Baptista

Supervisor: Prof. Eurico Gonçalves Assunção

Members of the Committee: Eur Ing Geoff Melton

Prof. Inês da Fonseca Pestana Ascenso Pires

May 2015

II

Resumo

Os processos de fabrico convencionais requerem uma quantidade elevada de maquinação para a remoção de

material, o que não satisfaz os requisitos de uma indústria moderna sustentável, de baixo custo e amiga do

ambiente.

Assim, Impressão 3D tem vindo a tornar-se um processo industrial revolucionário para a produção de

componentes metálicos no qual o desperdício de material é bastante reduzido. Neste processo são produzidos

componentes 3D por deposiçao de cordões em camadas.

Entre os inúmeros processos de Impressão 3D, Produção Aditiva por fio e arco,PAFA, tem a capacidade de

produzir componentes metálicos com volume reduzido, devido ao facto de ter grandes taxas de deposição. Por

sua vez, dentro dos processos PAFA, o processo de soldadura por arco eléctrico com protecção gasosa, GMAW,

tem vindo a destacar-se pelo sua eficiência bem como, por ter grandes taxas de deposição e proporcionar boas

propriedades mecânicas ao componente.

Durante este trabalho, estudou-se a utilização do processo Cold Metal Transfer, uma variante de GMAW que se

tem destacado em impressão 3D, com o objectivo de se comprender qual o seu potencial para a producão de

componentes 3D. O material utilizado foi uma liga de Niquel, pois este material é bastante utilizado na indústria

automóvel e aeroespacial e apresenta um custo relativamnete elevado.

Foram analisados dois parâmetros do processo CMT, corrente e velocidade de deslocamento, com o intuito de

se perceber qual a sua influência no resultado final, nomeadamente na taxa de deposição, a eficiência da

deposição, a largura média do componente, a área útil final, a dureza e, finalmente, a textura superficial. Durante

esta análise utilizou-se um software de design of experiments para confirmação e previsão do efeito destes

parâmetros nas respostas anteriormente referidas.

Os resultados obtidos permitem concluir de que forma os parâmetros analisados influênciam as características

dos depósitos obtidos.

Palavras chave: Impressão 3D, Produção Aditiva por fio e arco, GMAW, CMT (Cold Metal Transfer), Liga de

Niquel, Taxa de deposição, Eficiência de deposição, Área útil final, dureza, textura superficial.

III

Abstract

Conventional manufacturing processes often require a large amount of machining and cannot satisfy the

continuously increasing requirements of a sustainable, low cost, and environmentally friendly modern industry.

Thus, Additive Manufacturing (AM) has become an important and revolutionary industrial process for the

manufacture of custom-made metal work pieces. In this process 3D components are built by depositing beads of

weld in a layer by layer.

Among the different AM processes, Wire and Arc Additive Manufacture (WAAM) has the ability to

manufacture large, low volume metal work-pieces due to its high deposition rate. In turn, within the WAAM

processes, Gas Metal Arc Welding process (GMAW) has been highlighted by its efficiency, having large

deposition rates and provide good mechanical properties to the component, as well.

During this work, it was studied the use of CMT, a GMAW variant that has been highlighted in AM, with the

aim of understand its potential for 3D components manufacturing. The filler material used was a Nickel alloy,

which is commonly used in automobile and aerospace industry and has high cost.

Two CMT parameters have been analysed, which were current and travel speed, in order to understand their

effect in the final results which were the deposition rate, deposition efficiency, build-up average width, final

useful area, hardness and, finally, the surface texture. During this work a DoE software was used for

confirmation and perdition of the effect of these parameters on the responses previous referred. The results

obtained showed how the analysed parameters influence the characteristics of the deposits obtained.

Keywords: Additive Manufacturing, WAAM, GMAW, CMT (Cold Metal Transfer), Nickel alloy, Deposition

rate, Deposition efficiency, Final Useful Area, Hardness, Surface Texture.

IV

Agradecimentos

Em primeiro lugar quero apresentar os meus sinceros agradecimentos aos membros industriais do TWI os quais

financiaram este trabalho.

Ao professor Eurico Assunção e à professora Maria Luísa Coutinho pela orientação, apoio e espírito crítico

construtivo demostrado durante a realização deste trabalho bem como, pela oprtunidade de o realizar na empresa

TWI.

Ao Dr. Andrew Woloszyn por me ter aceite no Twi e ao Dr. Geoff Melton que me acompanhou, ajudou e

ensinou.

Ao Carne Willsher o qual realizou os ensaios experimentais, ao Ashley Spencer pelo auxílio durante a

preparação das amostras para análise, ao Ben Robison o qual realizou as medições da textura superficial, ao Dr.

Marcus Warwick pela ajuda com o software Design Expert e a toda a secção AWE pelo seu apoio.

Aos meus pais e irmã pelo o apoio incondiconal durante este tempo.

Ao meu namorado Zé pela paciência, pela visita a Cambridge, pelas horas de Skype e por todo o apoio em dias

bons e menos bons durante estes seis meses.

À Bea que me aturou e partilhou tudo e mais alguma coisa durante o tempo em Cambridge bem como, pelo seu

apoio.

Por fim, a todos os meus amigos pelo seu apoio, em especial ao Bica.

V

Contents

Resumo .......................................................................................................................................................... II

Abstract ....................................................................................................................................................... III

Agradecimentos ........................................................................................................................................... IV

List of Figures .............................................................................................................................................. IX

List of Tables ............................................................................................................................................. XIV

List of Nomenclature .................................................................................................................................. XV

1. Introduction........................................................................................................................................... 1

1.1. Background ............................................................................................................................... 1

1.2. Motivation ................................................................................................................................. 1

1.3. Objectives .................................................................................................................................. 2

2. Literature Review .................................................................................................................................. 3

2.1. Introduction .............................................................................................................................. 3

2.2. Additive Manufacturing History .............................................................................................. 3

2.3. Additive Manufacturing ........................................................................................................... 4

2.4. Additive Manufacturing with Metal ......................................................................................... 6

2.5. Wire and Arc Additive Manufacturing .................................................................................... 9

2.6. Gas Metal Arc Welding .......................................................................................................... 10

2.6.1. Short-circuiting transfer ......................................................................................................... 11

2.6.2. Pulsed arc transfer ................................................................................................................. 12

2.6.3. Globular transfer ................................................................................................................... 13

2.6.4. Spray transfer ........................................................................................................................ 13

2.7. Cold Metal Transfer, CMT ..................................................................................................... 14

2.8. Nickel Alloy ............................................................................................................................. 16

2.9. Summary ................................................................................................................................. 16

3. Experimental Approach ...................................................................................................................... 17

3.1. Introduction ............................................................................................................................ 17

3.2. Materials and Build-up appearance ....................................................................................... 17

3.3. Design of experiments, DoE .................................................................................................... 18

3.4. Procedure for perform the experimental trials ...................................................................... 19

3.5. Deposition Rate and Deposition Efficiency ............................................................................. 20

VI

3.5.1. Formulas ............................................................................................................................... 21

3.5.1.1. Deposition Rate, DR .......................................................................................................... 21

3.5.1.2. Deposition Efficiency, DE ................................................................................................... 21

3.5.1.3. Weight of Electrode used, WEU ......................................................................................... 21

3.5.1.4. Volume of electrode used, V ............................................................................................. 21

3.5.1.5. Length of wire used, LWU.................................................................................................. 21

3.6. Macrostructure ....................................................................................................................... 21

3.7. Width and Final Useful Area Measurements ......................................................................... 22

3.8. Hardness ................................................................................................................................. 23

3.9. Surface Texture ....................................................................................................................... 25

3.9.1. P-Profile Analysis ................................................................................................................. 27

3.10. Optimization ........................................................................................................................... 27

3.11. Summary ................................................................................................................................. 27

4. Equipment and Software..................................................................................................................... 28

4.1. Introduction ............................................................................................................................ 28

4.2. Welding Laboratory Equipment ............................................................................................ 28

4.3. Macrostructure Analysis Equipment ..................................................................................... 29

4.4. Hardness Analysis Equipment ................................................................................................ 31

4.5. Surface Texture Analysis Equipment ..................................................................................... 32

4.6. Software .................................................................................................................................. 32

4.6.1. Scentis .................................................................................................................................. 32

4.6.2. Ecos ...................................................................................................................................... 33

4.6.3. Alicona associated software ................................................................................................... 33

4.6.4. Design Expert 9 ..................................................................................................................... 34

4.7. Summary ................................................................................................................................. 34

5. Results and Discussion ........................................................................................................................ 35

5.1. Introduction ............................................................................................................................ 35

5.2. Experimental Tests results ...................................................................................................... 35

5.3. Experimental tests for Design Expert validation .................................................................... 36

5.4. Build-ups Appearance............................................................................................................. 36

5.5. Experimental data Obtained................................................................................................... 39

5.5.1. Experimental tests from Design Expert design ....................................................................... 39

VII

5.5.2. Experimental tests for Design Expert design validation .......................................................... 39

5.5.3. Heat Input ............................................................................................................................. 40

5.5.4. Current waveform ................................................................................................................. 41

5.6. Deposition Rate and Deposition Efficiency ............................................................................. 42

5.6.1. Deposition Rate ..................................................................................................................... 43

5.6.1.1. Deposition Rate Analysis on Design Expert ........................................................................ 43

5.6.2. Deposition Efficiency ............................................................................................................ 44

5.6.2.1. Deposition Efficiency analysis on Design Expert ................................................................. 45

5.7. Macrostructure ....................................................................................................................... 46



5.8. Average Width and Minimum Width ..................................................................................... 49



5.8.3. Average Width ...................................................................................................................... 53

5.8.3.1. Average Width Analysis on Design Expert .......................................................................... 54

5.8.4. Average Width Standard deviation......................................................................................... 55

5.8.4.1. Average Width Standard Deviation Analysis on Design Expert ............................................ 56

5.8.5. Minimum Width .................................................................................................................... 58

5.8.6. Minimum Width Standard Deviation ..................................................................................... 58

5.9. Final Useful Area .................................................................................................................... 59


5.9.2. Experimental tests for DoE design validation ......................................................................... 62

5.9.3. Final Useful Area .................................................................................................................. 63

5.9.3.1. Final Useful Area Analysis on Design Expert ....................................................................... 64

5.10. Hardness ................................................................................................................................. 66

5.10.1. Experimental tests from DoE ................................................................................................. 66


5.10.3. Average Hardness ................................................................................................................. 67

5.10.3.1. Average Hardness Analysis on Design Expert ................................................................. 68

5.11. Surface Texture ....................................................................................................................... 69

5.11.1. Experimental tests from DoE design ...................................................................................... 69

VIII

5.11.2. Experimental tests for DoE validation .................................................................................... 70

5.11.3. Average standard deviation and sample average standard deviation ........................................ 70

5.11.3.1. Sample Average of Side Average Standard deviation on Design Expert........................... 71

5.12. Optimization on Design Expert software ................................................................................ 77

5.12.1. Introduction........................................................................................................................... 77

5.12.1. Numerical Optimization ........................................................................................................ 77

5.13. Summary ................................................................................................................................. 78

6. Conclusions .......................................................................................................................................... 79

7. Recommendations ............................................................................................................................... 80

8. References............................................................................................................................................ 81

9. Appendix ............................................................................................................................................. 85

IX

List of Figures

Figure 2.1 – Working principle of AM. ............................................................................................................. 4

Figure 2.2 – Qualitative situation of the LM components production relative to usual options. ........................... 5

Figure 2.3 – Growth of rapid prototyping .......................................................................................................... 6

Figure 2.4 – Classification of AM for metals. .................................................................................................... 7

Figure 2.5 – Scheme of a generic powder bed system ........................................................................................ 7

Figure 2.6 – AM powder feed system. ............................................................................................................... 8

Figure 2.7 – Scheme of a wire feed unit. ........................................................................................................... 8

Figure 2.8 – 3D metallic component made by WAAM process. ....................................................................... 10

Figure 2.9 – GMAW process. ......................................................................................................................... 11

Figure 2.10 – GMAW equipment. ................................................................................................................... 11

Figure 2.11 – Cycle of short circuiting transfer. ............................................................................................... 12

Figure 2.12 – Scheme of pulsed arc transfer mode. .......................................................................................... 12

Figure 2.13 – Globular Transfer. ..................................................................................................................... 13

Figure 2.14 – Spray Transfer. ......................................................................................................................... 13

Figure 2.15 – Values of current and voltage for the different metal transfer modes. .......................................... 14

Figure 2.16 – CMT process scheme. ............................................................................................................... 14

Figure 2.17 – CMT welding phases. ................................................................................................................ 15

Figure 2.18 – Comparison of voltages and currents used in each metal transfer mode and in CMT process. ...... 15

Figure 3.1 – Sample appearance. ..................................................................................................................... 17

Figure 3.2 – Recommended consumables for dissimilar materials joining. ....................................................... 17

Figure 3.3 – Scheme of the method used to perform the build-up. .................................................................... 20

Figure 3.4 – First cut. ..................................................................................................................................... 22

Figure 3.5 – Second cut. ................................................................................................................................. 22

Figure 3.6 – Trace lines for width measurements. ............................................................................................ 22

Figure 3.7 – Method used for final useful area measurements. ......................................................................... 23

Figure 3.8 – Vickers hardness test scheme. ..................................................................................................... 24

Figure 3.9 – Surface texture profiles. .............................................................................................................. 25

Figure 3.10 – Direction of surface texture measurements. ................................................................................ 26

Figure 4.1 – Laboratory equipment scheme ..................................................................................................... 28

Figure 4.2 – Laboratory equipment ................................................................................................................. 28

Figure 4.3 – Kawasaki JS6.............................................................................................................................. 29

Figure 4.4 – AMV 5000 welding monitoring computer ................................................................................... 29

Figure 4.5 – First cut machine. ........................................................................................................................ 29

Figure 4.6 – Second cut machine. ................................................................................................................... 29

Figure 4.7 – Grinder machine ......................................................................................................................... 30

Figure 4.8 – Mounting machine. ..................................................................................................................... 30

Figure 4.9 – Manual polishing machine........................................................................................................... 30

Figure 4.10 – Automatic polishing machine. ................................................................................................... 30

X

Figure 4.11 – Fume Cabinet ............................................................................................................................ 31

Figure 4.12 – Acid Container .......................................................................................................................... 31

Figure 4.13 – Camera used for macrostructure analysis. .................................................................................. 31

Figure 4.14 – Machine used for hardness test. ................................................................................................. 31

Figure 4.15 – Machine used for surface texture analysis. ................................................................................. 32

Figure 4.16 – Scentis software ........................................................................................................................ 32

Figure 4.17 – Ecos software. ........................................................................................................................... 33

Figure 4.18 – Alicona associated software. ...................................................................................................... 33

Figure 4.19 – Design Expert 9 software. ......................................................................................................... 34

Figure 5.1 – Experimental test using 90A and 600mm/min. ............................................................................. 35

Figure 5.2 – Experimental test using 80A and 260 mm/min. ............................................................................ 35

Figure 5.3 – Test 1.......................................................................................................................................... 37

Figure 5.4 – Test 2.......................................................................................................................................... 37

Figure 5.5 – Test 3.......................................................................................................................................... 37

Figure 5.6 – Test 4.......................................................................................................................................... 37

Figure 5.7 – Test 5.......................................................................................................................................... 37

Figure 5.8 – Test 6.......................................................................................................................................... 37

Figure 5.9 – Test 7.......................................................................................................................................... 37

Figure 5.10 – Test 8 ........................................................................................................................................ 37

Figure 5.11 – Test 9 ........................................................................................................................................ 38

Figure 5.12 – Test 10 ...................................................................................................................................... 38

Figure 5.13 – Test 11 ...................................................................................................................................... 38

Figure 5.14 – Test 12 ...................................................................................................................................... 38

Figure 5.15 – Test 13 ...................................................................................................................................... 38

Figure 5.16 – Current and Travel Speed influence on Heat Input. .................................................................... 40

Figure 5.17 – CMT current waveform. ............................................................................................................ 41

Figure 5.18 – Current and Travel Speed influence on Deposition Rate ............................................................. 43

Figure 5.19 – Current and Travel Speed influence on deposition rate. .............................................................. 43

Figure 5.20 – Current and travel speed influence on Deposition Efficiency. ..................................................... 44

Figure 5.21 – Influence of current and travel speed on deposition efficiency. ................................................... 45

Figure 5.22 – Test 1 macro ............................................................................................................................. 46









Figure 5.31 – Test 10 macro ........................................................................................................................... 47

XI






Figure 5.37 – Good fusion between two layers. ............................................................................................... 49

Figure 5.38 – Poor fusion between two layers. ................................................................................................ 49

Figure 5.39 – Test 1 width measurements........................................................................................................ 50





Figure 5.44 – Test 6 width measurements ....................................................................................................... 50




Figure 5.48 – Test 10 width measurements. ..................................................................................................... 51




Figure 5.52 – Test 15 width analysis. .............................................................................................................. 52

Figure 5.53 – Test 16 width analysis. .............................................................................................................. 52

Figure 5.54 – Current and Travel Speed influence on Average Width. ............................................................. 53

Figure 5.55 – Relation between deposition rate and average width. .................................................................. 54

Figure 5.56 – Influence of current and travel speed on average width. ............................................................. 54

Figure 5.57 – Current and travel speed influence on average width standard deviation. .................................... 55

Figure 5.58 – Relation between Average width standard deviation and deposition efficiency. .......................... 56

Figure 5.59 – Coefficients significance. .......................................................................................................... 57

Figure 5.60 – Influence of current and travel speed on width standard deviation. ............................................. 57

Figure 5.61 – Current and travel speed influence on minimum width. .............................................................. 58

Figure 5.62 – Current and travel speed influence in minimum width standard deviation. .................................. 59

Figure 5.63 – Test 1 final useful area measurements. ....................................................................................... 59




Figure 5.67 – Test 5 area final useful area measurements................................................................................. 60





XII

Figure 5.72 – Test 10 final useful area measurements. ..................................................................................... 61






Figure 5.78 – Current and travel speed influence in final useful area. ............................................................... 63

Figure 5.79 – Relation between Final Useful Area and Average Width Standard Deviation.´ ........................... 64


Figure 5.81 – Influence of current and travel speed on final useful area. .......................................................... 65

Figure 5.82 – Indentation mark on the left and sample photo on the right. ........................................................ 67

Figure 5.83 – Hardness variation along build-up height ................................................................................... 67

Figure 5.84 – Influence of current and travel speed on average hardness. ......................................................... 68

Figure 5.85 – Current and travel speed influence on sample average of side average standard deviation. .......... 71


Figure 5.87 – Influence of current and travel speed on surface standard deviation. ........................................... 72

Figure 5.88 – Build-up surface distance variations to the surface roughness measurement average plan for test 1.

Top: face A, Bottom: face B. .......................................................................................................................... 73

















Figure 5.97 – Build-up surface distance variations to the surface roughness measurement average plan for test

10. Top: face A, Bottom: face B...................................................................................................................... 75





XIII


13. Left: face A, Right: face B. ....................................................................................................................... 76

Figure 5.101 – Relation between sides’ height profile and final useful area. ..................................................... 77

Figure 5.102 – Optimization graph. ................................................................................................................. 78

XIV

List of Tables

Table 2-1 – Various wire feed process. ............................................................................................................. 9

Table 2-2 – Chemical composition of IN625. .................................................................................................. 16

Table 3-1 – Characteristics of the substrate plate. ............................................................................................ 18

Table 3-2 – Wire technical features. ................................................................................................................ 18

Table 3-3 – Shielding gas features. ................................................................................................................. 18

Table 3-4 – Surface Texture parameter’s. ........................................................................................................ 26

Table 5-1 – Design of experiments generated by Design Expert....................................................................... 36

Table 5-2 - Design of experiments for Design Expert design validation. .......................................................... 36

Table 5-3 - Experimental data obtained for experimental tests from Design Expert design ............................... 39

Table 5-4 – Experimental data obtained for experimental tests for Design Expert design validation. ................ 40

Table 5-5 – Number of layers required and total heat input calculated for a 17mm height component. .............. 41

Table 5-6 - Calculations made for experimental tests from Design Expert design. ............................................ 42

Table 5-7 - Calculations made for experimental tests for Design Expert design validation. ............................... 42

Table 5-8 – Comparison between deposition rates obtained by experimental and by DoE................................. 44

Table 5-9 – Comparison between the deposition efficiencies obtained by experimental and DoE. .................... 46

Table 5-10 – Summary table of width analysis for experimental tests from Design Expert design..................... 52

Table 5-11 – Summary table of width analyses for experimental tests for Design Expert design validation. ...... 53

Table 5-12 – Comparison between the average widths obtained by experimental and by DoE. ......................... 55

Table 5-13 – Summary table for final useful area analyses for experimental tests from Design Expert design. .. 62

Table 5-14 – Summary table for final useful area analyses for experimental test for Design Expert design

validation. ...................................................................................................................................................... 62

Table 5-15 – Comparison between the final useful areas obtained by experimental and by DoE. ...................... 66

Table 5-16 – Average Hardness analysis for experimental tests from Design Expert design. ............................ 66

Table 5-17 – Average Hardness analysis for experimental tests for Design Expert design validation. ............... 67

Table 5-18 – Comparison between the average hardness’ obtained by experimental and DoE. ......................... 68

Table 5-19 – P profile parameters analysed and standard deviation averages for experimental test from Design

Expert design. ................................................................................................................................................ 69

Table 5-20 - P-profile parameters analysed and standard deviation averages for experimental experiments for

Design Expert validation. ............................................................................................................................... 70

Table 5-21 – Height´s profile range variation of each test for each side. ........................................................... 76

Table 9-1 – Whole data needed to deposition rate and deposition efficiency analysis for experimental tests from

Design Expert design. ..................................................................................................................................... 85

Table 9-2 – Whole data needed to deposition rate and deposition efficiency analysis for experimental tests for

Design Expert validation. ............................................................................................................................... 85

Table 9-3 – Whole data needed to average hardness analysis for experimental tests from Design Expert design86

Table 9-4 – Whole data needed to average hardness analysis for experimental tests for Design Expert validation.

...................................................................................................................................................................... 88

Table 9-5 - Whole data needed to surface texture for experimental tests from Design Expert design. ................ 88

Table 9-6 – Whole data needed to surface texture analysis for experimental tests for Design Expert validation. 90

XV

List of Nomenclature

AM – Additive Manufacturing

ALM – Additive Layer Manufacturing

RP – Rapid Prototyping

DMD – Direct Metal Deposition

FFF – Free Form Fabrication

HLM – Hybrid Layer Manufacturing

LM – Laser Melting

UAM – Ultrasonic Additive Manufacturing

EBM – Electron Beam Melting

PD – Plasma Deposition

GMAW – Gas Metal Arc Welding

PBAM – Powder Based Additive Manufacturing

WAAM – Wire and Arc Additive Manufacturing

CMT – Cold Metal Transfer

ASTM – American Society of Testing Materials

CAD – Computer Aided Design

SL – Stereo-Lithography

LOM – Laminated Object Manufacturing

FDM – Fused Deposition Modeling

3DP – 3D Printing

SLS – Selective Laser Sintering

DOE – Design of Experiments

DR – Deposition Rate

DE – Deposition Efficiency

WEU – Wire of Electrode Used

VEU – Volume of Electrode Used

LWU – Length of Wire Used

XVI

E - Voltage

I – Current

S – Travel speed

DoE – Design of Experiments

1

1. Introduction

1.1. Background

Additive manufacturing (AM) technologies have over 80 years of history and it is becoming an increasingly

important technology in the manufacturing field for direct fabrication of structural metal components. AM

technology is also known as Additive Layer Manufacturing (ALM), Rapid Prototyping (RP), Direct Material

Deposition (DMD), Free Form Fabrication (FFF), and Hybrid Layer Manufacturing (HLM) (Kruth et al. 1998).

AM techniques can be classified by the heat source utilised for melting/sintering the raw material, feedstock

material, feedstock delivery and system configuration, leading to denominations, such as Laser Melting (LM),

Ultrasonic Additive Manufacturing (UAM), Electron Beam Melting (EBM), Plasma Deposition (PD), Gas

Metal Arc Welding (GMAW), Powder Based Additive Manufacturing (PBAM), and finally, Wire and Arc

Additive Manufacturing (WAAM).

Nowadays, automotive and aerospace industries are continuously looking for applications that enable direct

production of complex full density metal parts, with low production volumes and high structural integrity. The

request for products and services from AM technology has been robust for more than twenty years (Wohlers T.

2010), leading to an increasing on the demand for WAAM process, in the lasts years. Thus, there has been a

growing interest in the research in the metal additive manufacturing field.

This growing interest is related with the advantages associated with this process. The main advantages are

minimum waste and tool-less production. The complete avoidance of tooling in AM techniques offers design

freedom and helps the design process to be further flexible, (Karunakaran et al. 2010).

The economic benefits and the environmental impact of AM processes used become even more significant in

the fabrication of components with high value material, such as nickel, when compared with traditional

machining processes. Nickel is an expensive material with growing use at about 4% each year, thus the use of

AM technology for Nickel parts is of interest in a large diversity of industrial sectors.

1.2. Motivation

Nickel is an expensive material used in a diversity of industries namely automobile and aerospace. The increase

of world consumption of nickel alloy the high cost of subtractive manufacturing technologies and the

environmental impact led to the consideration of an AM process as an alternative to manufacture nickel parts.

Due to the fact that arc welding is suitable for most commonly used metals and alloys, it was decided to look for

a process that fills this requirement. Among the GMAW process variants, it stands out a process denominated

Cold Metal Transfer process. This process is well known for good stability with all materials and precise

process control.

Accordingly to this the proposed project aims study of the CMT process in Additive Manufacturing of nickel

components.

2

1.3. Objectives

The objectives of the current project were:

Investigate the influence of current and travel speed in the build-up width, surface texture, hardness,

deposition rate, final useful area percentage and finally, deposition efficiency.

Optimize the process, finding the best combination of current and travel speed.

3

2. Literature Review

2.1. Introduction

In this chapter a review of the main fields related with the topic of this thesis is presented. It starts with a brief

summary of the Additive Manufacturing history with a description of the most popular Additive Manufacturing

process. After Additive Manufacturing technology it is explained, in detail, the application of this technology in

metals.

Moreover the Wire and Arc Additive Manufacturing process, the Gas Metal Arc Welding process, and finally

the Cold Metal Transfer process are introduced.

A brief introduction of Nickel alloys and its weldability analysis is presented in the last part of the literature

survey.

2.2. Additive Manufacturing History

Additive manufacturing, AM, is defined by ASTM as the “process of joining materials to make objects, usually

layer by layer, from 3D CAD data”. The most popular AM processes are Stereo-Lithography (SL), Laminated

Object Manufacturing (LOM), Fused Deposition Modeling (FDM), 3D Printing (3DP) and finally, Selective

Laser Sintering (SLS), (Kruth et al. 1998), which will be discussed in this chapter. Each technology is often

distinguished by the raw materials used or by the method of consolidation.

The first AM process appears in 1986 and was developed between 1986 and 1988 by 3D Systems (Wohlers &

Gornet 2011; Levy et al 2003; Wong & Hernandez 2012). The basic principle of this process is the photo

polymerization, which is the process where a liquid monomer or a polymer converts into a solidified polymer by

applying ultraviolet light which acts as a catalyst for the reactions. Briefly, this is a liquid-based process where

the curing or solidification of a photosensitive polymer occurs when an ultraviolet laser makes contact with the

resin. It starts with the design of the component in a CAD software and then this file is translated to a STL file

in which the pieces are “cut in slices” containing the information for each layer. A platform is built to anchor the

piece and supporting the overhanging structures. Then the UV laser is applied to the resin solidifying specific

locations of each layer. When the layer is finished the platform is lowered and finally when the process is done

the excess is drained and can be reused (Wong & Hernandez 2012).

In the same period of time the LOM process was developed. LOM is a process that combines additive and

subtractive techniques to build a part layer by layer. In this process the materials come in sheet form. The layers

are bonded together by pressure and heat application and using a thermal adhesive coating. A laser cuts the

material to the shape of each layer given the information of the 3D model from the CAD and STL file (Wong &

Hernandez 2012). The LOM concept was investigated by Nakagawa T. and Kunieda M. for laminated metal

sheet for tooling (Levy et al 2003).

Between 1988 and 1991 was developed by S. Scott Crump the FDM process which was commercialized by

Stratasys Inc. (Yan & Gu 1996; Levy et al. 2003). FDM is an additive manufacturing process in which a thin

filament of plastic feeds a machine where a print head melts it and extrude it in a thickness typically of 0.25mm.

It builds parts by depositing a stream of hot viscous material onto a base plate or previously deposited material

(Kruth et al. 1998; Wong & Hernandez 2012).

4

Since 1985 until 1997 was developed by MIT the 3D Printing process (Levy et al. 2003). It uses solid powder

material that is deposited in layers that are successively solidified by ink-jet printing droplets of binder onto the

powder material (Kruth et al. 1998).

In 1967-1992, SLS was developed by Carl Deckard and Joseph Beaman at the Mechanical Engineering

Department of University of Texas at Austin (Yan & Gu 1996). This is a three-dimensional printing process in

which a powder is sintered or fuses by the application of a carbon dioxide laser beam. The chamber is heated to

almost the melting point of the material. The laser fused the powder at a specific location for each layer

specified by the design. The particles lie loosely in a bed, which is controlled by a piston, that is lowered the

same amount of the layer thickness each time a layer is finished (Wong & Hernandez 2012).

AM technology has evolved significantly over the past three decades and by 2016, it is expected that AM

products and services reach $3.1 billion worldwide. By 2020, the industry is expected to hit $5.2 billion

(Wohlers T. 2011).

2.3. Additive Manufacturing

Additive Manufacturing is a process where parts are created from 3D CAD data by adding material layers upon

layers. The definition is opposite to subtractive manufacture methodologies (Hopkinson N. 2012), where parts

are manufactured by removal of material from raw blocks. In AM the component seems to grow from nothing to

completion. ‘’AM is also known as Additive Layer Manufacturing (ALM), Rapid Prototyping (RP), Direct

Material Deposition (DMD), Free Form Fabrication (FFF), Hybrid Layer Manufacturing (HLM)’’ (Karunakaran

et al. 2010).

The general working principle of AM is schematically represented in figure 2.1.

Figure 2.1 – Working principle of AM.

The first step is slicing the 3D geometric model into 2D layers, and the element of each layer is a 2D cross

section profile of the part. Then, each layer is built in a time and gets a near-net-shape component.

This technique started in the late 80’s and over the past decades, the development and application of AM has

been significantly increasing. Compared with traditional manufacturing technologies, AM has some significant

advantages as follow (Karunakaran et al. 2010):

5

Reduce material waste (compared with traditional method).

Can build near net-shape part with highly complex geometries directly from 3D CAD data without

tooling.

Parts show good mechanical properties (compared with casting).

Reduced leading time in the manufacturing process.

But the disadvantages of AM are as follow (Karunakaran et al. 2010):

Manufacture speed is slow compared with traditional methods at present.

The manufacturing process is difficult control.

The process is without tooling, but a substrate is necessary.

The surface quality is not excellent thus requires a finishing process.

This process is used to manufacture low quantities with medium to high geometric complexity as shown in

figure 2.2 (Levy et al. 2003).

Figure 2.2 – Qualitative situation of the LM components production relative to usual options.

Summary AM provides good material utilization, cost efficiency, a very energy-resourceful and environmental

approachable manufacturing way and a near net shape after part machining.

The aerospace industry is a vital sector embracing additive manufacturing, as this process can realise massive

savings of expensive material, give design freedom and manufacture more complex components easily. This

explains the increase on the demand and research in this technology which is proved by the graph presented in

figure 2.3 (Wohlers, 2010). As can be seen in figure 2.3 there is a growth in this technology with no signal to

decrease in system sales.

6

Figure 2.3 – Growth of rapid prototyping

2.4. Additive Manufacturing with Metal

Due to the AM advantages such as, saving material, no tooling, enhancing complexity of the components,

cutting back the cost of manufacturing and environmentally friendly, this technology becomes a promising

alternative for manufacturing components made of expensive materials or difficult to machine materials, where

waste expect to be minimized (Mehnen et al. 2010).

AM for metal process is divided in direct process and indirect process (Karunakaran et al. 2010). An AM

technology is nominated indirect process when a casting process is involved in layer by layer processes,

otherwise it is a direct process.

With regard to how the additive material is supplied, currently popular AM technologies can be classified as

either a powder-feed process or a wire-feed process. The powder-feed process is capable of fabricating parts

with small size and high geometrical accuracy. On the other hand, the wire-feed approach is a cleaner and more

environmental friendly process, which does not expose operators to the potentially hazardous powder

environment (Ding et al. 2015).

Depending on the energy source used, AM for metals can be classified into three groups, namely laser based, arc

welding based, and electron beam based (Ding et al. 2015).

In figure 2.4 is presented the classification of AM for metal process.

Sales

Year

7

Figure 2.4 – Classification of AM for metals.

When the feed material used is a powder based material there are two feed systems possibilities, which are

powder feed system and powder bed system (Frazier 2014).

Figure 2.5 show a scheme of a generic powder bed system. A powder bed is created by raking powder across the

work area. The energy source (electron beam or laser beam) is programmed to deliver energy to the surface of

the bed melting or sintering the powder into the desired shape. Additional powder is raked across the work area,

and the process is repeated to create a 3D component (Frazier 2014).

Figure 2.5 – Scheme of a generic powder bed system

A generic illustration of AM powder feed systems is shown in figure 2.6.

In these systems, powders are conveyed through a nozzle onto the build surface. A laser is used to melt a

monolayer or more of the powder into the shape desired. This process is repeated to create a solid three

dimensional component (Frazier 2014).

Energy Source

Feed Process Material

Process

AM technologyAdditive

Manufacturing for Metals

Direct

(layer by layer)

Powder based

LaserElectron

beam

Wire based

Arc

Indirect

(casting)

8

Figure 2.6 – AM powder feed system.

As previously referred the powder-feed process is capable of fabricating parts with small size and high

geometrical accuracy. In addition, it is possible to produce parts with functionally graded materials (FGM)

(Ding et al. 2015).

The major advantages of the powder based methods are the precision, accuracy and surface finish obtained

which in some situations are near to machined surfaces. On the other hand, the main disadvantages of this

process are porosity problems which appear because of the porosity in the powder, and restricted deposition

rates because with the increase in the powder feed rate there is likelihood of formation of pores (Choi & Chang

2005). However, to improve the density of the product, heat treatment is essential (Rombouts et al. 2006).

A scheme of a wire feed unit is shown in figure 2.7. The feedstock is wire, and the energy source for these units

can include electron beam, laser beam, and arc. Initially, a single bead of material is deposited and upon

subsequent passes is built upon to develop a three dimensional structure. In general, wire feed systems are well

suited for high deposition rate processing and have large build volumes; however, the fabricated product usually

requires more extensive machining than the powder bed or powder fed systems do (Frazier 2014).

Figure 2.7 – Scheme of a wire feed unit.

Compared with the powder-feed process, it has higher material usage efficiency with up to 100% of the wire

material deposited into the component. Additionally, metal wires are lower in cost and more readily available

9

than metal powders having suitable properties for AM, making wire-feed technology more cost-competitive

(Ding et al. 2015).

Table 2-1 presents various existing technologies of wire feed process (Karunakaran et al. 2010).

Table 2-1 – Various wire feed process.

Energy Source Wire feed process

Laser

Direct metal deposition (DMD)

Directed light fabrication (DLF)

Laser additive manufacturing (LAM)

Laser based direct metal deposition (LBDMD)

Rapid direct metal deposition

Electron beam Electron beam freeforming

Arc

3D welding

Hybrid layered manufacturing

Hybrid plasma deposition and milling (HPDM)

Shape deposition manufacturing

Among wire feed process, arc welding based AM has shown promise due to its combined advantages of higher

deposition rate, energy efficiency, safe operation and lower cost. Generally, the deposition rate of laser or

electron beam deposition is in the order of 2–10 g/min, compared with 50–130 g/min for arc welding based AM

technology. Laser is commonly used as the energy source in the AM system. However, it has very poor energy

efficiency (2–5%). Electron beam has a slightly higher energy efficiency (15– 20%), but it requires a high

vacuum working environment. Compared with the poor energy efficiency of laser and electron beam, the energy

efficiency of arc welding processes such as the gas metal arc welding (GMAW) or gas tungsten arc welding

(GTAW) processes can be as high as 90% in some circumstances (Ding et al. 2015). To increase the deposition

rate and mechanical properties these welding processes have been studied.

As a result, Wire and Arc Additive Manufacturing (WAAM) using either the GMAW or the GTAW process is a

promising technology for manufacturing components with medium to large size in terms of productivity, cost-

competitiveness and energy efficiency (Colegrove et al. 2013).

Among the Arc process, shaped deposition manufacturing has a high potential in the fabrication of complex

structures especially in aerospace components (Clark et al. 2008).

2.5. Wire and Arc Additive Manufacturing

Wire and arc additive manufacturing (WAAM) process combines the arc as heat resource with wire feeding, to

create 3D components. The concept of WAAM has actually been around for almost a century – the first patent

was filed in 1920 by Baker (Baker 1925). However, it was until the last two decades that this idea was

extensively developed with modern welding and automation technologies, which has received distinctive

consideration since it has an advantage over the traditional manufacturing techniques (Lorant, 2010).

Over the past years, a number of studies have investigated the utilisation of arc welding methods, i.e. GTAW

and GMAW. WAAM Standard wire based welding processes such Gas Metal Arc Welding (GMAW) and Gas

10

Tungsten Arc Welding (GTAW) are low cost solutions employed as heat source providing high deposition rate

by utilising high energy input. That is the reason why the demand for WAAM has been on the rise over the past

decades (Adebayo et al. 2013).

WAAM methods have the potential for manufacturing of big volume parts with high structural integrity at

reduced cost. (Mehnen et al. 2010) point out that by integrating welding deposition process with grinding

process in one machine and using of new welding technologies such as CMT (Cold Metal Transfer) or Inter

pulse Welding, the WAAM technique can be used to manufacture high quality components with precisely

defined surface geometries.

Some research works have been carried out by many universities. A research program named Wire and Arc

Additive Manufacturing (WAAM) has been done at Cranfield University

As shown in figure 2.8, in the WAAM process 3D metallic components are built by feeding wire metallic

material and depositing beads of weld metal in a layer by layer way.

Figure 2.8 – 3D metallic component made by WAAM process.

2.6. Gas Metal Arc Welding

The evolution of Gas Metal Arc Welding (GMAW) technologies has become more significant for automation

and control of welding (Hudson 2004).

The GMAW process, figure 2.9, appeared in the 1920s, but only in 1948 was this process fully developed and

ready to be used by all types of industries. This process is an arc welding process that uses an electric arc

between a continuous filler material and the weld pool. In order to realize a weld this process needs the aid of

shielding gases that are supplied externally. These gases can be inert, Metal Inert Gas - MIG, or can be active,

Metal Active Gas – MAG.

11

Figure 2.9 – GMAW process.

This welding process has significant advantages when compared with other welding processes, it can weld all

types of commercially available metals and alloys, it can weld in all positions, it does not have the same

restrictions in electrode’s dimensions when compared with shielded metal arc welding, it has higher welding

speeds, does not need heavy slag removal operations. But it has its own limitations, it needs a more complex and

more costly equipment, figure 2.10, than shielded metal arc welding and needs to be protected against air drafts,

because of the shielding is performed by gases that can be blown away.

Figure 2.10 – GMAW equipment.

In this process exists four main metal transfer modes, which are short-circuiting, pulsed arc, globular and spray.

2.6.1. Short-circuiting transfer

Short circuiting transfer (Santos & Quintino 1993), also called dip transfer, in GMAW is determined by low arc

current and voltage, and characterized by low heat input. As a result of lower current and consequently heat

input this type of transfer mode is used to weld thin materials. This type of metal transfer produces a fast freeze

weld pool. The metal is transferred from the electrode to the work piece only during the time the electrode

contacts with the weld pool. There are about twenty to two hundred contacts per second between the electrode

and the weld pool. The complete cycle of short circuiting transfer is represented in figure 2.11.

12

Figure 2.11 – Cycle of short circuiting transfer.

There are three major phases in short circuiting metal transfer. The first phase, short, embraces the points A to

D, of figure 2.11, corresponds to an increase in welding current and voltage, when the molten tip of the

electrode is attached to the welding pool, ensuring a quick transfer of the molten drop to the weld pool. The

second phase, reignition, occurs on point E, of figure 2.11, the voltage is increased suddenly and it is when the

pinching forces separate the electrode melted tip from the weld pool. Finally, the third phase, arcing period,

occurs between point E and I of figure 2.11, the tip of the electrode is separated from the weld pool and the

beginning of a melted tip starts. In this phase the current needs to be as low as possible to avoid spatter

formation.

2.6.2. Pulsed arc transfer

For this transfer mode the high current pulses may be obtained from a single phase rectifier connected to a

rectifier power source as used for dip transfer. With pulse transfer the welding current alternates between high

and low levels. A high current density detaches droplets of metal and a low current density maintains the arc. By

this means a form of controlled spray transfer is obtained at low current values. The process is shown in figure

2.12.

Figure 2.12 – Scheme of pulsed arc transfer mode.

13

2.6.3. Globular transfer

Globular transfer, represented schematically in figure 2.13, it is often seen as the worst transfer mode in

MIG/MAG welding, because it tends to produce high heat, poor weld surface and spatter. This transfer mode is

characterized by drop size with a diameter bigger than the electrode diameter. This type of transfer occurs with

relatively low current. The main electrode forces acting in the droplet detachment are the pinching force and the

anode reacting force.

Figure 2.13 – Globular Transfer.

2.6.4. Spray transfer

With a current level above the transition current and with Argon shielding gas, it’s possible to achieve the

transition between globular transfer and spray transfer. This type of metal transfer, figure 2.14, it is

characterized by small droplets transferred between the electrode and the work piece, and by spatter free welds.

These small droplets are transferred at a rate of hundreds by second and are accelerated by electromagnetic

forces. This type of metal transfer mode can achieve deeper penetrations than other transfer modes. The spray

transfer is not used to weld thin sheets or to make root passes, because it needs high levels of current and

voltage, and so makes a big weld pool and tends to distort thin metal sheets.

Figure 2.14 – Spray Transfer.

In figure 2.15 is presented the different voltages and intensities used in all the different GMAW processes. The

correspondence between colors and the transfer mode is orange for spray transfer mode, grey for globular

transfer mode, green for pulsed arc transfer mode and light blue for short-circuiting transfer mode.

14

Figure 2.15 – Values of current and voltage for the different metal transfer modes.

2.7. Cold Metal Transfer, CMT

The technological developments carried out in GMAW concerned the development of new power sources with

different waveform designs, such as Cold Metal Transfer (CMT).

In 2005, Fronius introduced a revolutionary new arc welding process called CMT. CMT is classified as a dip

transfer process and characterized by low heat input when compared to the conventional GMAW (Fronius

2005). The CMT presents an innovative solution, which is determined by the motion of the electrode directly

assisted by the process control mechanism. As described by Fronius (2005), when the arc plasma is developed

the filler wire moves to the weld pool until the wire touches the weld pool and short-circuiting takes place. Then

the current becomes lower and the electrode is retracted enhancing the droplet detachment.

This forward and backward motion takes place at a frequency of up to 70 Hz. The control system detects the

short-circuiting and then retracts the filler wire after droplet transfer. This process is schematized in figure 2.16.

Figure 2.16 – CMT process scheme.

The CMT process has two major phases, the arcing phase and the sort circuit phase, as show as figure 2.17

(Pickin & Williams 2011). This figure illustrates the current and voltage waveform which characterize the CMT

process.

Pulsed Arc

Short-circuiting

15

Figure 2.17 – CMT welding phases.

The first phase, the Arcing phase, is characterized by an increase in voltage and a significant increase in welding

current. In this phase a molten droplet is formed on the end of the wire electrode and a weld pool is created.

After a set duration of time, the welding current decreases. This dropping in the welding current prevents the

globular transfer mode to occur. This value of current is maintained until the short circuit begins.

Finally, in the short circuit phase, the wire electrode is fed forward contacting the weld pool and the voltage

drops to zero. In this moment, in conventional MIG/MAG, the current rises and the electrical pinching forces

separate the electrode from the weld pool. Contrary, in CMT process, the current stays at low levels and the wire

is sent in a backward motion and a force is transmitted that separates the electrode from the welding pool. The

arc is then reignited and the cycle repeats.

Benefits of this new process include the possibility of simultaneous dip transfer and pulse arc welding, with heat

input lower than conventional MIG/MAG welding (Fronius 2005) and no spatter.

The lower heat input of this process can be observed in figure 2.18 that shows the different voltages and currents

of each metal transfer mode and of CMT process.

Figure 2.18 – Comparison of voltages and currents used in each metal transfer mode and in CMT process.

16

2.8. Nickel Alloy

Nickel and nickel alloys are among the toughest structural materials known. Nickel and nickel alloys are non-

ferrous metals with high strength and toughness, excellent corrosion resistance, and superior elevated

temperature properties.

In this thesis the filler material used was a nickel alloy Inconel 625. Table 2-2 presents the chemical

composition of this nickel alloy.

Table 2-2 – Chemical composition of IN625.

Alloy 625 Ni Cr Fe C Mn Si Mo Al Ti Nb P S

Min bal 21.0 - - - - 8.0 - - 3.2 - -

Max bal 23.0 4.0 0.025 0.40 0.40 10.0 0.40 0.40 3.8 0.010 0.010

Inconel 625 microstructure is formed by a Ni matrix with cubic face centred structure. This alloy has found

extensive use in many industries for diverse applications over a wide temperature range from cryogenic

conditions to ultra-hot environments over 1000◦C.

The alloy is endowed with good combination of yield strength, creep strength, fatigue strength and excellent

oxidation and corrosion resistance in aggressive environments. Moreover, its good weldability and procesability

foster its choice for many applications (Dinda et al. 2009). The main disadvantage of the use of these alloys is

its high cost, being used only when cheaper materials do not provide the necessary properties of wear resistance

required for special applications engineering.

The main problem associated with the nickel weldability is cracking. This problem is linked with heat input and

is associated with the concentration of Laves phase particles in the weld.

High heat input results in relatively slow cooling which causes the formation of a large volume of Laves phase

particles. Then, cracking can be avoided by reducing heat input during the welding process (Wilson et al. 1991).

2.9. Summary

In this chapter a brief history of Additive manufacturing and its used in metal manufacture were presented. Wire

and Arc Additive Manufacturing technology was introduced and Gas Metal Arc Welding process was discussed

with special attention as the Cold Metal Transfer process.

Finally a brief introduction of Nickel alloys and its weldability analysis was presented.

17

3. Experimental Approach

3.1. Introduction

In this chapter the experimental procedure is defined and explained in greater detail. It’s here that fixed

conditions of the process and variable parameters are defined. It is also defined the design measures of the build-

up and decisions made along each analysis.

3.2. Materials and Build-up appearance

In this project it was decided to build and compare several samples obtained by multi-layer linear welds,

commonly known as build-up. These build-ups were made on the top of a substrate plate, like shown in figure

3.1. The predefined dimensions for the build-ups are 120mm length and 20mm height.

Figure 3.1 – Sample appearance.

Using a nickel alloy, specifically Inconell 625, for substrate plate composition is a too expensive choice, so it

was decided to use a dissimilar material. According with figure 3.2, the recommended base material for using

with Inconel 625 is an austenitic stainless steel. The best and most widely used austenitic stainless steel is 304L

stainless steel. This material is cost effective when compared with Inconel 625 so it was chosen for the

constituent material of the substrate plate.

Figure 3.2 – Recommended consumables for dissimilar materials joining.

The substrate plates dimensions and their characteristics are presented in table 3-1.

18

Table 3-1 – Characteristics of the substrate plate.

Material Density, 𝝆

[g/cm3]

Length

[mm]

Width [mm] Thickness

[mm]

Weight [Kg]

All

plates

Plate

n6

Plate

n10

All

plates

Plate

n6

Plate

n10

304l stainless steel 8.03 250 50 56 52 10 1.003 1.12 1.043

The weight presented in table 3-1 was calculated based on 304l stainless steel density and on the volume of each

plate, according with the follow equation.

𝜌 = Mass

Volume

The filler material used to perform the build-ups was a nickel alloy with the physical characteristics shown on

table 3-2.

Table 3-2 – Wire technical features.

Material Type Density [g/cm3] Diameter [mm] Section Area [cm2]

Inconel 625 Wire 8.44 1 0.0079

The wire diameter was chosen based on the push-pull system. The wire sizes that work well on a push-pull

system vary in a range from 0.8 mm to 1.6 mm [1]. Once the focus is on finer detail the smallest practicable

diameter was chosen, corresponding to a small deposition. When a 0.8 mm wire diameter was used there were

some difficulties to feed the wire. Consequently, 1 mm diameter was tried and the feed was performed with no

problems. Thus, for this project a wire diameter of 1 mm was chosen.

For shielding gas pure Argon was chosen because it is recommended its use for the Nickel MIG welds [2].The

values of concentration and gas flow is present on table 3-3. These values are fixed and constant for all tests. A

gas flow of 20L/min was used so that during the tests the protection of the entire build-up was done, instead of

the protection of only the layer that was being made.

Table 3-3 – Shielding gas features.

Type Concentration [%] Gas Flow [L/min]

Argon 100 20

3.3. Design of experiments, DoE

After establishing all the fixed parameters, it was necessary to decide the variable parameters, which are the

current and the travel speed. In order to study the influence of these two parameters on the process, several

aspects will be analysed, the surface texture, average width, final useful area percentage, average hardness,

deposition rate and finally, deposition efficiency.

The values of the current on CMT process varies in a range of 80A to 130A (Dutra et al. 2013), and the travel

speed assumes values between 200mm/min and 600mm/min (Adebayo et al. 2013).

19

The first phase of the experimental setup was to decide how many samples are needed.

Considering a variation interval of 10A for current and 50mm/min for travel speed, it is concluded that 54

samples are needed. Since, to produce 54 samples would require the use of many resources such as material and

time, so it was decided to use an appropriate software that reduces significantly the number of samples required

for performing the same study. The software chosen was Design Expert software developed by Ease-Sat.

Design of experiments (DOE) is a powerful tool that can be used in a variety of experimental situations. DOE

allows for multiple inputs, factors, to be manipulated, determining their effect on a desired output. By

manipulating multiple inputs at the same time, DOE can identify important interactions that may be missed

when experimenting with one factor at a time.

Summarizing, introducing on the Design Expert software the operating range of the variable parameters of this

work, current and travel speed, as inputs, it is generated a design of experiments where based on the data

obtained for each response for each sample, predicts the influence of the variable parameters on the responses

previously referred.

Subsequently, are presented the steps made on Design Expert software. The first step was generate the design of

experiments. For this it was necessary, at first, to choose the study type, design type and introduced the range

values of each input. Bearing in mind that one of the project objectives is to optimize the process, a Response

Surface Methodology, RSM, was chosen for study type, due to this type of study be an experimental technique

to find the optimal response within specified ranges of the factors. After this, it was chosen the design type, was

chosen an optimal design for the same reason. Then, the variable parameters ranges were introduced and it was

generated by the software a design of experiments. Following this it was performed the second step that consists

on the analysis of each response. When the analysis is finished it was performed the third step, the optimization.

When is used a software of this kind is necessary to do its validation. This validation of the software consist on

performing a couple of experimental trials with different input values of the ones generated by Design Expert,

and compare the responses obtained, with the ones predicted by the software. In this project two experimental

trials were chosen.

3.4. Procedure for perform the experimental trials

After the first step of the design of experiments, the laboratory work phase was initiated.

On a first stage, the method to perform the build-up was planned. The scheme presented in figure 3.3 illustrates

the selected method. The sequence of points indicates the path made by robot.

20

Figure 3.3 – Scheme of the method used to perform the build-up.

The path performed by robot influences the solidifying structure, the thermal gradient and the tensions generated

by the deposition. In reverse direction depositions, the grain growth direction which follows heat flow, changes

every layer disrupting the growth of the dendrites. Stress generated during deposition by the thermal gradient

also vary depending on the trajectory used, and with the change of direction between layers, tensions along the

deposition tends to be lowers (Alberti et al. 2014).

The next step, after selecting the method to perform the build-up, is programming this path on robot. A fixed

step of 3mm between each layer was programed.

The average voltage and current, and the welding time were recorder in each experimental test.

Several passes were performed until the build-up reaches approximately 20 mm in height. The following

procedure was undertaken in each experimental trial:

Place the plate on the work space

Turn on the robot

Start program

Remove the sample from work space

Measure the sample weight

3.5. Deposition Rate and Deposition Efficiency

The deposition rate is the rate that weld metal can be deposited by a given electrode or welding wire, expressed

in kilograms per hour. It is based on continuous operation, not allowing time for stops and starts caused by

inserting a new electrode, cleaning slag, termination of the weld or other reasons.

The deposition efficiency is the weight of the weld metal deposited compared to the weight of the electrode

consumed, expressed as a percentage.

In this chapter the formulas used for the deposition rate analysis and deposition efficiency analysis are

presented.

21

3.5.1. Formulas

3.5.1.1. Deposition Rate, DR

DR [Kg

h] =

Weight after − Weight before

Welding time (1)

3.5.1.2. Deposition Efficiency, DE

DE[%] =Weight after − Weight before

Weight of Electrode consumed∗ 100 (2)

3.5.1.3. Weight of Electrode used, WEU

WEU [Kg] = IN625 Density ∗ Volume of electrode used (3)

3.5.1.4. Volume of electrode used, V

V[cm3] = Section Area of the wire ∗ lenght of wire used (4)

3.5.1.5. Length of wire used, LWU

LWU [cm] = welding time ∗ wire feed speed (5)

3.6. Macrostructure

In this project a destructive welding test was used in order to evaluate the characteristics of the weld.

Destructive welding test, as the name suggests, involves the physical destruction of the completed weld. This

method of testing is used frequently for a number of applications. Some of these applications include welding

procedure qualification and welder performance qualification testing, sampling inspection of production welds,

research inspection, and failure analysis work.

The destructive test used in this project was a Macro etch test. This test allows see a cross section of the weld,

and observe the arrangement of the grains in the weld material, in other words, the macrostructure of the weld. It

can also show defects such as porosity, inclusions and poor fusion.

This test was performed through 6 steps which were, cutting a sample from the welded build-up, deburr the

edges, mount in a resin, grinding and polishing, etching, and finally inspection.

Cutting: This step consists in cutting a sample from the build-up sample previous done. Two cuts were

performed. The first one, marked with two red lines in figure 3.4., consists on cut a portion from the original

sample discarding the beginning and end portions. The second one, highlighted with two red lines in figure 3.5,

consists on cut the excessive material of the substrate plate.

22

Figure 3.4 – First cut.

Figure 3.5 – Second cut.

Deburring the edges: Eliminate the metal deburr that resulted from cutting process.

Hot Mounting: Mounting the sample on a phenolic hot mounting resin with carbon filler.

Grinding and polishing: Grinding the samples using progressively finer grades and after performing at first

manual polishing and secondly automatic polishing during 10 min.

Etching: The sample is placed inside a container with an acid solution. After a short time, the welded areas will

begin to discolour. When it doesn't occur, the procedure has to be repeated. If it discolours too much, it may

require re-polishing and reapplication of the acid. Once results are visible, the sample is rinsed off and carefully

dried.

Inspection: In this stage a picture which shows the number of the layers and the defects along the build-up is

taken.

3.7. Width and Final Useful Area Measurements

In this chapter the procedures to perform the width measurements and the final useful area measurements are

discussed.

Using the image resultant of macro test and with the support of Scentis software, the necessaries measurements

were performed.

For the width measurement, the previous image resultant from macro was opened on Scentis software and

several lines along the welded build-up were drawn. These lines were strategically traced in areas of greater

width and reduced width as shown in figure 3.6.

Figure 3.6 – Trace lines for width measurements.

23

When a line is drawn, the software automatically calculates the length of the line and displays it in the image as

can be seen in figure 3.6.

According to the goal of this project, it was necessary find a value that represents the width variation along the

build-up and that allows its comparison with the other samples. Thus, it was chosen to calculate the average

width and the width standard deviation, for each sample. For these calculations the width variation along the

first layer was negligible since, the width increased gradually from the bottom to the top of the layer.

Similarly, for the final useful area measurements, it was imported on Scentis software the same image provided

by macro test. Thereafter polygons were drawn over the area required to remove. For this step it was established

a machining depth equal to 0.5 mm since is considered a reasonable dimension. As it can be seen in figure 3.7,

this distance was established in relation to the most inner point of each side.

Figure 3.7 – Method used for final useful area measurements.

In the same way that the Scentis software calculates the width, it calculates the area of the drawn polygon. This

measurement is considered an approximation since there exists an error associated with the act of drawing the

polygon over the image. Then, it was summed the values of the removed areas required, which represent the

total removed area required. On the other hand, knowing the width and the height of the remaining section, the

inner rectangle, it was calculated the final area value. Adding these two values it was obtained the value for total

area of the build-up.

Due to the differences in heights obtained on each sample, it becomes important to find a value that could be

comparative. Thus, the final useful area according to the following expression was calculated.

Total Area [mm2] = Removed Area + Final Area (6)

Final Useful Area [%] =Final Area

Total Area× 100 (7)

3.8. Hardness

In this project in order to analyse the hardness of each a Vickers hardness test sample was used. This is a

method to measure the hardness of materials that is often easier to use than other hardness test since, the

required calculations are independent of the size of the indenter. In this test the indenter can be used for all

24

materials independent of the hardness. The indenter composition is diamond and has a form of a square-based

pyramid, as shows figure 3.8.

Figure 3.8 – Vickers hardness test scheme.

The test was performed using a load of 5 Kg during 10 seconds. Twelve indentations on each sample were

performed, 11 along the build-up and 1 on the substrate. It was used a 5 Kg load because for this material this is

an industry standard load.

This test was performed on appropriate equipment for this analysis and the value of the diagonals from each

indentation are shown on the software associated with this equipment, ECOS. Knowing the value of both

diagonals for each indentation it was calculated the hardness value for each indentation and then, the average

hardness for the build-up. This average represents the average hardness value of the sample, which will be used

to make the comparison with the other samples.

The formulas used for the hardness value calculation are present below.

Area

A =d2

2 × sen (136

2)

=d2

1.8544 (8)

d [mm] =(d1 + d2)

2 (9)

Vickers Hardness

HV =F[Kgf]

A [mm2]↔ HV =

1.8544 × 5 × 105

((𝑑1 + 𝑑2)

2)

2 (10)

Average Hardness

Average HV =∑ HV

Number of identations made in the weld (11)

25

3.9. Surface Texture

The term “surface texture” refers to the fine irregularities, peaks and valleys, produced by the forming process,

and their direction on the surface (Song, J.F.). This term, also known as surface topography or surface finish,

can be viewed from two different perspectives. From the machinist’s perspective, texture is a result of the

manufacturing process and, from the designers perspective, surface texture is a condition that affects the

functionality of the part.

On analysis, surface texture is defined by three characteristics which are lay, surface roughness and waviness.

Lay is the direction of the predominant surface pattern ordinarily determined by the production method used.

Surface roughness commonly shortened to roughness is a measure of the finely spaced surface irregularities.

Finally, waviness is the measure of surface irregularities with spacing greater than that of surface roughness.

The surface profile is measured two-dimensionally using a tracing system. The unfiltered primary profile, P-

profile, is the actual measured surface profile. Filtering this last profile in accordance with international

standards leads to the waviness profile, W-profile, and the roughness profile, R-profile. It can be seen in figure

3.9.

Figure 3.9 – Surface texture profiles.

Surface texture parameters were separated into three groups depending on the type of profile from which they

are calculated. P parameters are calculated on the primary Profile, R parameters are calculated on the roughness

profile and, W parameters are calculated on the waviness profile. In table 3-4 are presented the three surface

texture parameters groups.

26

Table 3-4 – Surface Texture parameter’s.

P parameters W parameters R parameters

Pa Average height of profile Wa Waviness average Ra Average roughness of

profile

Pq Root-Mean-Square height

of profile

Wq Root-Mean-Square

waviness of profile

Rq Root-Mean-Square

roughness of profile

Pt Maximum peak to valley

height of primary profile

Wt Maximum peak to valley

height of waviness profile

Rt Maximum peak to valley

height of roughness profile

Pz Mean peak to valley height

of primary profile

Wz Mean peak to valley height

of waviness profile

Rz Mean peak to valley height

of roughness profile

Pp Maximum peak height of

primary profile

Wp Maximum peak height of

waviness profile

Rp Maximum peak height of

roughness profile

Pv Maximum valley height of

primary profile

Wv Maximum valley height of

waviness profile

Rv Maximum valley height of

roughness profile

Pc Mean height of profile

irregularities of primary

profile

Wc Mean height of profile

irregularities of waviness

profile

Rc Mean height of profile

irregularities of roughness

profile

Psm Mean spacing of profile

irregularities of primary

profile

l Profile length l Profile length

l Profile length lc Cut off wavelength lc Cut off wavelength

Initially, it was considered to analyse the waviness along the height direction and the surface roughness along

the length direction, as shown in figure 3.10, but, due to problems of filtering data correctly, it was decided to

analyse only the primary profile along the height direction, in other words, analyse the waviness profile and the

roughness profile together.

Figure 3.10 – Direction of surface texture measurements.

Length direction Hei

ght

dir

ecti

on

27

3.9.1. P-Profile Analysis

This analysis was performed using equipment appropriated for this type of analysis. The sample was positioned

on the equipment and it was scanned along a stated length. It was chosen a sample length of 2 cm, because it is

the maximum practical length that can be measured.

The scanned image was sent to a software associated with the equipment, Alicona associated software, where its

analysis was performed. It was drawn a line which represents the path that is analysed by the software. In this

analysis 3 lines were drawn to give an average of the surface profile. After drawing the lines, the software

obtained automatically the values of the parameters for the profile under study. Thereafter, their analysis was

performed.

Using this machine it’s only possible to obtain a scanned image of one of the build-up sides, at one time. Thus,

it is only possible to analyse one side at a time and consequently, get the values of the main parameters for only

one of the sides, at a time. It is important to find a calculation method to cross the information obtained for each

side, in order to obtain a single value that represents the sample and that can be comparative. It was decided to

calculate an average of the main parameters for each side and then calculate an average value of the two average

values obtained for each side. This last value represents the sample and will be the comparative value for this

analysis.

3.10. Optimization

The optimization it was the last step performed on this work. In this, it was performed a combination of the

whole results obtained for each test and it was got the optimum combination of the variable parameters under

study. It was used Design Expert software for this phase. The optimization on this software can be performed in

two ways, numerically and graphically.

Design-Expert software’s numerical optimization maximizes, minimizes, or targets a single response, subject to

upper and/or lower boundaries on other responses or combinations of two or more responses. After choosing the

goal for each response, the program constructs desirability indices. The desirability range is from 0 to 1 for any

given response. The program combines the individual desirability into a single number and then search for the

greatest overall desirability. A value of one represents the ideal case. A zero indicates that one or more

responses fall outside desirable limits.

On graphical optimization by shading out regions that fall outside of specified contours, it is possible identify

desirable sweet spots for each response – windows of opportunity where all specifications can be met.

3.11. Summary

In this chapter the experimental guidelines followed during this work were presented as the all decisions made.

28

4. Equipment and Software

4.1. Introduction

In this chapter it is explained in detail the equipment used during the experiments developed during this project.

The welding equipment, the recorder equipment, the macrostructure and surface texture analysis equipment and

finally, the software’s used.

The goal of this chapter is to give the main characteristics of the equipment and software used during this

project.

4.2. Welding Laboratory Equipment

The equipment used for perform the samples are described below.

It was used a total of three different equipment’s which are the CMT machine, Kawasaki robot and the recorder

equipment. In figure 4.1 is presented the scheme of the laboratory equipment and in figure 4.2 the real image of

the whole laboratory equipment.

Figure 4.1 – Laboratory equipment scheme

Figure 4.2 – Laboratory equipment

The CMT Advanced equipment comprises the following parts:

CMT Power source

Remote Control Unit

Cooling Unit

Wire feed unit

Wire buffer

Push-pull torch

The robot used was a Kawasaki JS6, shown on figure 4.3.

29

During the build-ups performance it was necessary to measure the current, voltage and verify if the waveform of

these parameters was in their regular form. To acquire this information the AMV 5000 equipment was used as

shown in figure 4.4. This is a welding monitoring computer that can be used to acquire recorded data.

Figure 4.3 – Kawasaki JS6

Figure 4.4 – AMV 5000 welding monitoring computer

4.3. Macrostructure Analysis Equipment

For the macrostructure analysis the first step was to cut the samples twice. During this work all the cuts were

wet cuts, where the liquid used is an oil based coolant.

For the first cut, which cut a sample from the original sample, the Struers Unitom-2 was used as shown in figure

4.5. This machine has a speed of 2775 rpm and it was used a 350x2.5x32mm abrasive cut-off wheel.

For the second cut which cut the excessive material on the substrate plate the Brilliant 220 was used as shown

figure 4.6. This machine has a speed of 3300 rpm and uses 150 mm abrasive cut-off wheels.

Figure 4.5 – First cut machine.

Figure 4.6 – Second cut machine.

The second step was deburring the edges of the sample. For this step it was used the Phoenix 3000, figure 4.7.

This grinder has a range of speeds from 50 rpm’s to 1200 rpm’s. The samples were grinded with a speed of 150

rpm’s. In this process a disk with 305 mm of diameter with a grain size of 60 was used.

30

The third step was mounting the sample into a phenolic hot mounting resin with carbon filler. For this step the

Struers LaboPress-3 was used, figure 4.8. This process was performed using a load of 50 KN, with a heating

time of 9 minutes at 180C and a cooling time of 9 minutes as well.

Figure 4.7 – Grinder machine

Figure 4.8 – Mounting machine.

The fourth step was grinding and polishing the samples already mounted in the resin. For grinding, it was used

the grinder shown in figure 4.7. The samples were ground in a range of speeds from 150 rpm’s to 300 rpm’s.

The sequence of the grain size of the disks with 305 mm diameter used was 60, 120, 320, 600, 1200 and 2500.

After grinding, the samples were polished. At first it was performed a manual polishing and secondly an

automatic polishing. The manual polishing was performed using the BUEHLER MetaSer2000, figure 4.9. This

polisher has a speed range from 50 rpms to 500 rpms. The speed used during the polishing was 250 rpm. The

automatic polishing was performed using ATA Saphir 560, figure 4.10. The polish was performed using a load

of 20N during 10 min. The set speed was 300 rpm for the base and 150 rpm for the top.

Figure 4.9 – Manual polishing machine.

Figure 4.10 – Automatic polishing machine.

The fifth step was electrolytic etching of the sample. This process was made inside of Chemcap Ductless Fume

Cabinet, figure 4.11. The sample is placed inside a container which contains an acid solution, figure 4.12. This

solution is 20% sulphuric acid and 80% water. A range from 2.5V to 4V was used.

31

Figure 4.11 – Fume Cabinet

Figure 4.12 – Acid Container

The last step was the inspection. This step consists in taking an image from the cross section surface of the

sample. The equipment used was Leica DFC295, as show figure 4.13.

Figure 4.13 – Camera used for macrostructure analysis.

4.4. Hardness Analysis Equipment

The equipment used to perform the hardness test was the Struers Duramin-A300, figure 4.14. This test was

performed using a load of 5 Kg during 10 seconds.

Figure 4.14 – Machine used for hardness test.

32

4.5. Surface Texture Analysis Equipment

For this analysis Alicona InfiniteFocus SL3D surface profilometer was used, figure 4.15, with a 5x objective

lens, and an associated software IF Measure Suite Software.

Figure 4.15 – Machine used for surface texture analysis.

4.6. Software

During this project were used several different software’s which are, Scentis, Ecos, Alicona associated software

and Design Expert 9.

4.6.1. Scentis

After conducted the macro sections it was necessary to find the real dimensions of the build-up, in order to

perform the width analysis and the final useful area analysis. This software can measure the distances by draw

lines or areas over the image that is being analysed, previous sized. This sized is achieved with help of one

known dimension, in this case a ruler scale graduation incorporated on the provided image from macro. The user

trace a line between two point of the ruler graduation and inputs how much is its real length. Thus, the software

can make a relation between the distances in the image and the real distances. In figure 4.16, it is possible to see

the Scentis software with a macro section image and the scale already inserted.

Figure 4.16 – Scentis software

33

4.6.2. Ecos

The next software used was Ecos. This software is associated with Struers Duramin-A300 equipment. This

software measures the two diagonals of each indentation on the sample and displays them on the screen. In

figure 4.17, it is possible to see the Ecos software which presents an indentation image and the values of the

diagonals of each indentation already performed.

Figure 4.17 – Ecos software.

4.6.3. Alicona associated software

For the surface texture analysis it was used Alicona software. This is Alicona Infinite Focus SL3D surface

profilemeter associated software. This software scans the sample surface and by tracing a line on the scanned

image it measures the surface waviness, roughness or both. In figure 4.18, is possible to see the Alicona

associated software with a scanned image of build-up surface.

Figure 4.18 – Alicona associated software.

34

4.6.4. Design Expert 9

The software widely used during this project was the Design Expert 9, shown in figure 4.19. This is a design of

experiments software which is used to optimize a process. This software was used during this work to generate

the design of experiments and analyse and optimize the whole responses under study.

Figure 4.19 – Design Expert 9 software.

4.7. Summary

In this chapter the equipment´s and software´s used during this work were overviewed and described.

35

5. Results and Discussion

5.1. Introduction

In this chapter all data obtained during the project is presented and discussed.

5.2. Experimental Tests results

After generating the first design of experiments on DoE software tests were carried out. Figure 5.1 shows the

first test performed, in which the current had a value of 90A and the travel speed was of 600mm/min. For this

combination of parameters weld bead humping occurs, figure 5.1. Weld bead humping is a high speed welding

defect generally described as the phenomenon of formations of humps in a bead at regular intervals during

welding. It can also be described as a periodic undulation consisting of a hump and a valley. This defect is

consequence of high travel speeds (Adebayo et al. 2013). Therefore, it was decided to change the travel speed

range maintaining constant the current range. A new travel speed range, which assumed values from 200

mm/min to 300 mm/min, was used.

Introducing this new travel speed range on DoE, a new design of experiments was generated. Figure 5.2 shows

the test in which the current is 80A and the travel speed is 260 mm/min.

Figure 5.1 – Experimental test using 90A and 600mm/min.

Figure 5.2 – Experimental test using 80A and 260 mm/min.

As can be seen in Figure 5.2, there is a discontinuity in the formation of the first weld bead. Such defect is

justified by the utilization of low current value which correspond to low wire feed speed. At low wire feed

speeds, occurs feeding difficulties due to the stiffness of the wire [3]. Thus, it was concluded that 80A is a low

current value for this process using this material.

Consequently, it was decided to change the current range instead of the travel speed range. A current range from

90A to 130A was chosen.

Introducing this new current range a new design of experiments was generated, being this the final version. This

design is shown in table 5-1.

Hump Valley

36

Table 5-1 – Design of experiments generated by Design Expert.

Test Number Current, [A] Travel Speed, [mm/min]

1 130 300

2 110 200

3 130 200

4 90 200

5 130 300

6 110 300

7 90 300

8 90 200

9 90 300

10 100 245

11 120 250

12 90 300

13 130 200

5.3. Experimental tests for Design Expert validation

Based on the design of experiments that will be analysed, two tests were chosen for validation. The tests chosen

are presented in table 5-2.

Table 5-2 - Design of experiments for Design Expert design validation.

Test Number Current, [A] Travel Speed, [mm/min]

15 100 200

16 100 300

5.4. Build-ups Appearance

In this section the appearance of the all build-ups performed is shown.

37

Figure 5.3 – Test 1








38






Following examination of the samples in figure 5.6, 5.9, 5.11, 5.14, it was observed that the same type of defect

was present. This defect can be described as a lack of fusion, causing disbonding of the build-up from the

substrate, which occurs due to insufficient fusion between the first weld bead and the substrate plate. Figure 5.9,

5.11 and 5.12 have the same variable parameters values, current of 90A and high travel speed of 300mm/min.

The low current implies insufficient penetration/fusion (de Resende et al. 2009), which is likely to be the main

reason for the disbonding defect. When cooling occurs the material shrinks, in consequence of the insufficient

penetration the disbonding defect appear. The sample presented in figure 5.6, which has a current and a travel

speed of 90A and 200mm/min, the disbonding effect occurs less pronounced when compared with the other

three test previous referred. At lower welding speeds there is an increase in deposition rate and in heat input in

the same period of time. The higher heat input leads to a lower cooling speed and thus to a decrease in

shrinkage. This decrease in the shrinkage is the main reason for the decrease on the pronouncement of this

defect (Raj. Baldev et al., 2002).

39

5.5. Experimental data Obtained

During the experimental tests performing were measured a few number of parameters, that characterize each

sample, which were the wire feed speed, weight after welding, welding time, number of passes and finally, the

current and voltage. Knowing the current, voltage and travel speed of each test, it was calculated the heat input

according to the following equation.

Heat Input [KJ

mm] =

60 × E × I

1000 × S× 0,8 (12)

Where E is voltage [V], I is current [A], S is travel speed [mm/min].

5.5.1. Experimental tests from Design Expert design

Table 5-3, shows the data recorded during the test and the heat input calculated for each layer, for the

experimental tests from DoE design.

Table 5-3 - Experimental data obtained for experimental tests from Design Expert design

Test

Number

Current,

[A]

Travel

Speed,

[mm/min]

Wire

feed

speed

[m/min]

Weight [Kg] Welding

Time

[min]

Number

of

passes

AMV Heat

Input,

[KJ/mm] Before

Welding

After

Welding

Current

[A]

Voltage

[V]

1 130 300 6.9 1.003 1.14 3.28 8 101.9 11.9 0,19

2 110 200 5.8 1.003 1.14 3.69 6 99.6 12 0,29

3 130 200 6.9 1.003 1.12 3.6 6 102.8 12.1 0,30

4 90 200 4.5 1.003 1.12 4.31 7 84.7 11.6 0,24

5 130 300 6.9 1.003 1.12 2.91 7 100.4 11.7 0,19

6 110 300 5.8 1.12 1.24 3.30 8 97.9 11.9 0,19

7 90 300 4.5 1.003 1.08 3.27 8 84.1 11.9 0,16

8 90 200 4.5 1.003 1.12 4.27 7 84.1 11.9 0,24

9 90 300 4.5 1.003 1.08 3.28 7 84.3 11.9 0,16

10 100 245 5.1 1.043 1.14 3.54 7 96.3 12.6 0,24

11 120 250 6.5 1.003 1.16 3.97 8 104 12.4 0,25

12 90 300 4.5 1.003 1.08 2.93 7 84 12 0,16

13 130 200 6.9 1.003 1.16 3.7 6 107 12.8 0,33

5.5.2. Experimental tests for Design Expert design validation

Table 5-4, shows the data recorded during the test performed and the heat input calculated for each layer, for the

experimental tests for DoE design validation.

40

Table 5-4 – Experimental data obtained for experimental tests for Design Expert design validation.

Test

Number

Current,

[A]

Travel

Speed,

[mm/min]

Wire

feed

speed

[m/min]

Weight [Kg] Welding

Time

[min]

Number

of

passes

AMV Heat

Input,

[KJ/mm] Before

Welding

After

Welding

Current

[A]

Voltage

[V]

15 100 200 5.1 1.003 1.14 4.27 6 97 12.6 0,29

16 100 300 5.1 1.003 1.10 3.2 7 95 12.8 0,19

Table 5-3 and table 5-4, shows that an increase in current leads to an increase in wire feed speed.

Analysing the values measured by AMV for current and travel speed, a discrepancy between the measured

current and the set current was observed. It can be explained by the difference between the sapling rate and

averaging technique used by each equipment, since the current waveform is very irregular and difficult to

analyse. However the real value of current and voltage is the one measured by AMV, because it is the one that is

correctly calibrated.

5.5.3. Heat Input

In this section the influence of current and travel speed on the heat input is shown, figure 5.16, and the heat

input provided for each test was discussed.

Figure 5.16 – Current and Travel Speed influence on Heat Input.

To quantify the influence of travel speed on heat input the trend line was analysed. This trend line, equation 14,

was obtained in excel maintaining current constant. Based on equation 13 and equation 14 it was observed that

the travel speed and current has the same influence on heat input than current, once the slope is equal.

𝑦 = 0.0008𝑥 + 0.09 (13)

𝑦 = −0.0008𝑥 + 0.4 (14)

Due to the defects that may occur associated to high heat input, previous referred in chapter 2.8., an analysis to

quantify the heat input provided to each test until achieve a reference height was performed. As the minimum

0,15

0,20

0,25

0,30

0,35

80 100 120 140

Hea

t In

pu

t, [

KJ/

mm

]

Current, A

Heat Input

C 130 TS 300

C 130 TS 200

C 120 TS 250

C 110 TS 300

C 110 TS 200

C 100 TS 245

C 90 TS 200

C 90 TS 300

y= 0,0008x + 0,0905

41

height reached during the tests was 17mm, this value was considered as the reference point for this analysis. The

number of layers necessaries in each test to achieve this height of 17mm was counted. Then, the total heat input

provided for each test was calculated, table 5-5.

Table 5-5 – Number of layers required and total heat input calculated for a 17mm height component.

Test Number Current, [A] Travel Speed,

[mm/min]

Number of Layers

required

Total Heat Input,

[KJ/mm]

1 130 300 7 1,36

2 110 200 5 1,43

3 130 200 6 1,79

4 90 200 6 1,41

5 130 300 7 1,32

6 110 300 7 1,30

7 90 300 7 1,12

8 90 200 6 1,44

9 90 300 6 0,96

10 100 245 6 1,43

11 120 250 7 1,73

12 90 300 7 1,13

13 130 200 5 1,64

Analysing table 5-5, it was observed that tests characterized by a current of 90A and a travel speed of

300mm/min presented the lowest heat input.

5.5.4. Current waveform

Using the current values recorded for a test with a current of 110A and a travel speed of 200mm/min, it was

obtained the general current waveform for this process. Figure 5.17 show this current waveform.

Figure 5.17 – CMT current waveform.

0

50

100

150

200

250

0,48 0,5 0,52 0,54 0,56 0,58

Cu

rren

t (A

)

Time (s)

CMT Current Waveform

Current (A)

42

As expected the current waveform obtained presented the two phases that characterized the CMT process, the

arcing phase, represented by the red rectangular, and the short-circuiting phase, represented by green

rectangular.

In the arcing phase there is a big increase of current and after a set duration of time the current decrease to avoid

the globular transfer mode occur.

In the short-circuiting phase the current drops and stay at low levels. After this phase the cycle repeats.

5.6. Deposition Rate and Deposition Efficiency

After heat input analysis, it were calculated the deposition rate and deposition efficiency for each test.

In this chapter the deposition rate and deposition efficiency calculated for experimental tests from DoE design is

shown in table 5-6. The deposition rate and deposition efficiency calculated for experimental tests for DoE

design validation is shown in table 5-7. In appendix is introduced table 9-1 which presented the whole

calculations performed.

Table 5-6 - Calculations made for experimental tests from Design Expert design.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Deposition Rate,

[Kg/h]

Deposition Efficiency,

[%]

1 130 300 2.51 91.32

2 110 200 2.23 96.62

3 130 200 1.95 71.06

4 90 200 1.63 91.05

5 130 300 2.42 88.03

6 110 300 2.11 91.38

7 90 300 1.41 78.94

8 90 200 1.65 91.94

9 90 300 1.41 78.70

10 100 245 1.64 81.09

11 120 250 2.37 91.83

12 90 300 1.58 88.19

13 130 200 2.55 92.77

Table 5-7 - Calculations made for experimental tests for Design Expert design validation.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Deposition Rate,

[Kg/h]

Deposition Efficiency,

[%]

15 100 200 1.93 94.91

26 100 300 1.82 89.66

43

5.6.1. Deposition Rate

This analysis was based on the results obtained for experimental tests from DoE design.

Figure 5.18 – Current and Travel Speed influence on Deposition Rate

The point signalized by the red ellipse in figure 5.18, it was negligible for this analysis because wasn’t

consistent with the trend of the other test.

Based on figure 5.18 it was observed that an increase in current leads to an increase in deposition rate. This is

explained by the following reasoning:

TS =Const and ↑ C ↑ WFS ↑ material fed per unit of time ↑ material deposited per unit of time

Thus, the deposition rate is directly proportional to the current.

Regarding travel speed, this parameter only has influence in the material deposited per unit of length. This is

explained by the following reasoning:

C =Const WFS = Const, when ↑TS ↓ material deposited per unit of lenght.

The maximum deposition rate occurs for a test with a current of 130A and a travel speed of 200mm/min.

So to maximize deposition rate high currents should be used.

5.6.1.1. Deposition Rate Analysis on Design Expert

In this section is introduced the influence of current and travel speed on deposition rate, predicted by DoE. This

influence can be seen in figure 5.19.

Figure 5.19 – Current and Travel Speed influence on deposition rate.

1

1,5

2

2,5

3

80 90 100 110 120 130 140

Dep

osi

tion R

ate,

[K

g/h

]

Current, [A]

Deposition RateC 130 TS 300

C 130 TS 200

C 120 TS 250

C 110 TS 300

C 110 TS 200

C 100 TS 245

C 90 TS 200

C 90 TS 300

𝑦= 0,0253𝑥 − 0,7975

Design-Expert® SoftwareFactor Coding: ActualDeposition Rate (Kg/h)

Design Points2.55

1.41

X1 = A: CurrentX2 = B: Travel Speed

90 100 110 120 130

200

220

240

260

280

300

Deposition Rate (Kg/h)

A: Current (A)

B: T

rave

l Spe

ed (m

m/m

in)

1.6

1.82 2.2 2.4

2

2

44

Based on figure 5.19, it is possible to see that DoE predicted the same influence of current on deposition rate.

Thus, it was confirmed that the deposition rate is directly proportional to the current. Due to the inclination of

contour lines, it was also confirmed that the DoE prediction isn´t correct since the contour lines should be

verticals.

However DoE design validation was performed. For tests for DoE design validation, the values obtained for

deposition rate are shown on table 5-7. Table 5-8 shows the comparison between the deposition rates obtained

by experimental and DoE.

Table 5-8 – Comparison between deposition rates obtained by experimental and by DoE.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Experimental Deposition

rate, [kg/h]

DoE Deposition rate,

[kg/h]

15 100 200 1.93 ≈ 2

16 100 300 1.82 ≈ 1.80

Table 5-8 shows that the DoE prediction for deposition rate is correct once the results obtained were close to the

ones obtained experimentally. Thus, the DoE prediction for deposition rate is validated.

5.6.2. Deposition Efficiency

Figure 5.20 shows the influence of current and travel speed in deposition efficiency.

Figure 5.20 – Current and travel speed influence on Deposition Efficiency.

Analysing carefully the graph presented in figure 5.20, it was observed that the deposition efficiency is directly

proportional to the current until a value of 110A and inversely proportional for higher values of current from the

same value. As the current increases the heat input increases so the material is continuously heating. As shown

in figure 5.20 the optimum case occurs for a current of 110A which present the highest deposition efficiency.

From this value onwards the increase in heat input leads to material evaporation and consequently to the

decrease in deposition efficiency. Also for high currents arc instability occurs (Palani & Murugan, 2006) which

is proportionally related to the amount of spatters (Kang et al., 2013). So for currents above 110A arc instability

occurs leading to an increase in spatters and consequently to a decrease in deposition efficiency.

Regarding travel speed, it was observed that an increase in travel speed leads to a light decrease in deposition

efficiency. So, the deposition rate is inversely proportional to travel speed.

60

70

80

90

100

80 90 100 110 120 130 140

Dep

osi

tion E

ffic

iency

, [%

]

Current, [A]

Deposition EfficencyC130 TS 300

C 130 TS 200

C 120 TS 250

C 110 TS 300

C 110 TS 200

C 100 TS 245

C 90 TS 200

C 90 TS 300

y=-0.014x2+3.183x-85.25

45

Keeping the current constant, an increase in travel speed leads to a decrease in heat input. Then, with the

decrease in heat input the material doesn’t achieve an optimum heating point leading to a decrease in deposition

efficiency.

Test 3, signalized by a red ellipse in figure 5.20, is the one who isn’t in agreement with these conclusions

perhaps because, might have occurred a measurement error or an error during the test performing. For this

reason it was negligible.

The maximum deposition efficiency occurs for a test with a current of 110A and a travel speed of 200mm/min.

It’s known that this process has deposition efficiencies greater than 80% (Ding, J., 2012). For the deposition

efficiency calculations it was necessary calculate the length of wire used. This value was obtained through the

multiplication of welding time by wire feed speed.

The welding time measured and used was the time needed to perform the build-up from the beginning to the

end. Actually, it should be used the arcing phase time because represents the real time in which is consuming

wire. Using this time, the actual length of wire used is less than the one calculated and consequently the

deposition efficiency is greater than the one obtained.

5.6.2.1. Deposition Efficiency analysis on Design Expert

In this section is introduced the influence of current and travel speed on deposition efficiency, predicted by DoE

software. This influence can be seen in figure 5.21.

Figure 5.21 – Influence of current and travel speed on deposition efficiency.

Based on figure 5.21, the previous analysis made for the deposition efficiency variation with travel speed and

current is correct. It was confirmed that the deposition efficiency is inversely proportional to travel speed.

Relative to current, it was confirmed that is directly proportional to current until 110A and inversely

proportional from the same value.

For the performed tests for DoE design validation, the values obtained for deposition efficiencies are shown on

table 5-7. Table 5-9 shows the comparison between the deposition efficiencies obtained by experimental and

DoE.

Design-Expert® SoftwareFactor Coding: ActualDeposition Efficiency (%)

Design Points96.62

78.7


90 100 110 120 130

200

220

240

260

280

300

Deposition Efficiency (%)

A: Current (A)

B: Tra

vel S

peed (m

m/m

in)

85

90

93

95

96

2

22

46

Table 5-9 – Comparison between the deposition efficiencies obtained by experimental and DoE.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Experimental Deposition

Efficiency, [%]

DoE Deposition

Efficiency, [%]

15 100 200 94.91 ≈95

16 100 300 89.66 ≈86

Table 5-9 shows that the DoE prediction for deposition efficiency is correct once the results obtained were close

to the ones obtained experimentally. Thus, the DoE prediction for deposition efficiency is validated.

In order to maximize this parameter must be used currents close to 110A and low travel speeds.

5.7. Macrostructure

After analysis of the set data obtained during the experimental tests performed, it was performed a

macrostructure analysis. Through this analysis it was possible see the existent defects on the build-up cross-

section.


In this chapter the macros obtained for experimental tests from DoE design are presented.

Figure 5.22 – Test 1 macro


47









48





In this chapter the macros obtained for experimental tests for DoE design validation are presented.



Observing the corresponding macros for each test, shown in figure 5.22 to figure 5.36, it was possible to see and

confirm how many layers make up each test. It was observed that test 3, figure 5.24, has a defect, a gap, near the

base of the build-up. This defect is due to the bulging of the second layer over the first. On test 9, figure 5.30,

there is a pore in the last layer.

49

Through macros it is also possible to identify poor and good fusion between layers. When the contact angle

between two consecutive layers is less than 90 degrees, it is considered poor fusion. Contrary, when the angle is

greater than 90 degrees, it is considered good fusion. Figure 5.37 and figure 5.38, show some examples of good

fusion and poor fusion between layers, respectively.

Figure 5.37 – Good fusion between two layers.

Figure 5.38 – Poor fusion between two layers.

Looking into more detail the macros for each test, it was possible to see in whole tests that there are areas of

poor fusion and good fusion. In some test this effect is more pronounced than in other. It was observed that there

is a relation between the quality of fusion and the variable parameters, which are the current and the travel

speed. This is explained by the following reasoning obtained by macros comparison:

C= Const. and ↑TS ↑ possibility of the poor fusion effect between layers

TS=Const. and ↑C ↓ possibility of the poor fusion effect between layers

So the poor fusion effect is directly proportional to the travel speed and inversely proportional to current. To

optimize these two parameters high currents and low speed travel must be used.

It was also observed that, for whole samples, the first layer is always very narrow. The main reason for this

occurs is on the substrate thickness. When the first layer is performed the heat is conducted to the substrate large

cross area leading to an insufficient heating on the first layer. Thus, the first layer must be performed using a

different set of parameters to avoid too small widths in the first layer or used a thin substrate plate.

5.8. Average Width and Minimum Width

Following the macrostructure the average width and minimum width analysis was carried out. These analyses

consists on evaluate the width variation along the welded build-up and the width standard deviation and find a

correlation between these two parameters and the variable parameters.


In this chapter the measurements performed for experimental tests from DoE design are presented.

50

Figure 5.43 – Test 5 width measurements.

Figure 5.44 – Test 6 width measurements





51








52

Table 5-10 shows the whole significant values needed for this analysis.

Table 5-10 – Summary table of width analysis for experimental tests from Design Expert design.

Test

Number

Current,

[A]

Travel

Speed,

[mm/min]

Average

Width,

[mm]

Average Width

Standard

Deviation, [mm]

Minimum

Width,

[mm]

Minimum Width

Standard

Deviation, [mm]

1 130 300 7.71 0.86 6.08 1.90

2 110 200 8.13 0.54 7.52 0.84

3 130 200 9.62 0.64 8.97 0.94

4 90 200 5.83 0.95 4.36 1.81

5 130 300 7.63 0.50 7.34 0.58

6 110 300 6.72 0.64 5.66 1.28

7 90 300 4.96 0.69 4.01 1.21

8 90 200 6.51 0.78 5.60 1.24

9 90 300 5.21 1.44 3.50 2.31

10 100 245 7.58 0.79 6.60 1.29

11 120 250 8.4 0.80 7.58 1.17

12 90 300 5.21 0.50 4.51 0.88

13 130 200 9.23 0.60 8.55 0.92


In this chapter the measurements performed for experimental tests for DoE design validation are presented.

Figure 5.52 – Test 15 width analysis.

Figure 5.53 – Test 16 width analysis.

For the experimental tests used for DoE validation, only the average width and the average width standard

deviation were analysed, table 5-11.

53

Table 5-11 – Summary table of width analyses for experimental tests for Design Expert design validation.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Average Width,

[mm]

Average Width Standard

Deviation , [mm]

15 100 200 7.65 0.56

16 100 300 6.61 0.71

5.8.3. Average Width

Based on the average width for each test, presented in table 5-8, it was created a graph, figure 5.54, for a better

understanding of the results obtained.

Figure 5.54 – Current and Travel Speed influence on Average Width.

Analysing figure 5.54, it was observed that an increase in current leads to an increase in the average width, and

an increase in travel speed leads to a decrease in average with. So the average width is directly proportional to

current and inversely proportional to travel speed.

The maximum value obtained occurs for a test with a current of 130A and a travel speed of 200mm/min, high

current and low travel speed.

It is also important to point that the current has more influence in average width than the travel speed, proven by

the trend line slope of equation 19 and equation 20 for current and travel speed, obtained in excel for each

parameter maintaining the other constant.

𝑦 = 0.0668𝑥 − 0.9058 (19)

𝑦 = −0.0141𝑥 + 10.95 (20)

It was also observed that there is a relation between deposition rate and average width. As shown in figure 5.55,

an increase in current leads to an increase in deposition rate and in average width. This can be explained by the

following reasoning:

TS =Const and ↑ C ↑ WFS ↑ material fed per unit of time ↑ material deposited per unit of time

↑ weld bead width ↑ average width

4

5

6

7

8

9

10

80 90 100 110 120 130 140

Aver

age

Wid

th, [m

m]

Current, [A]

Average Width

C 130 TS 300

C 130 TS 200

C 120 TS 250

C 110 TS 300

C 110 TS 200

C 100 TS 245

C 90 TS 300

C 90 TS 200

𝑦 = 0.0668𝑥 − 0.9058

54

Figure 5.55 – Relation between deposition rate and average width.

5.8.3.1. Average Width Analysis on Design Expert

In this section the influence of current and travel speed on average width, predicted by DoE is shown in figure

5.56.

Figure 5.56 – Influence of current and travel speed on average width.

Based on figure 5.56, it is possible to see that DoE predicted the same influence of current and travel speed on

average width. Thus, it was confirmed that the average width is directly proportional to current and inversely

proportional to the travel speed.

Due to the inclination of contour lines, it was confirmed that the current has a higher influence on average width

variation than the travel speed.

0

2

4

6

8

10

1

1,5

2

2,5

3

80 90 100 110 120 130 140

Aver

age

Wid

th, [

mm

]

Dep

osi

tion

Rat

e, [

Kg/h

]

Current, [A]

Deposition Rate vs Average Width

Deposition Rate C 130 TS 300 Deposition Rate C 110 TS 300

Deposition Rate C 90 TS 300 Average Width C 130 TS 300

Average Width C 110 TS 300 Average Width C 90 TS 300

Design-Expert® SoftwareFactor Coding: ActualAverage Thickness (mm)

Design Points9.23

4.96


90 100 110 120 130

200

220

240

260

280

300Average Thickness (mm)

A: Current (A)

B:

Tra

ve

l S

pe

ed

(m

m/m

in)

6

7

8

9

2

22

Average Width (mm)

55

For the performed tests for DoE design validation, the values obtained for average width are shown on table 5-

11. Table 5-12 shows the comparison between the average widths obtained by experimental and DoE.

Table 5-12 – Comparison between the average widths obtained by experimental and by DoE.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Experimental Average

Width, [mm]

DoE Average Width,

[mm]

15 100 200 7.65 [7,8]

16 100 300 6.61 [6,7]

Table 5-12 shows that the DoE prediction for deposition rate is correct once the results obtained were conform

to the ones obtained experimentally. Thus, the DoE prediction for deposition rate is validated.

5.8.4. Average Width Standard deviation

Based on the average width standard deviation for each test, in table 5-8, it was created a graph, figure 5.57, for

a better understanding of the results obtained.

Figure 5.57 – Current and travel speed influence on average width standard deviation.

Figure 5.57 shows that an increase in current leads to a decrease in average width standard deviation until a

current close to 110A and to an increase from this value.

Regarding travel speed, it was observed that an increase in travel speed leads to a light increase in average width

standard deviation.

For this analysis the point symbolized by the red ellipse, test 9 shown in figure 5.47, it was negligible due to its

no consistency with the two other tests which present the same variable parameters. The same occurs for the test

marked by the red square, test 1 figure 5.39.

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

80 90 100 110 120 130 140

Aver

age

Wid

th S

tan

dar

d D

evia

tion

,

[mm

]

Current, [A]

Average Width Standard Deviation

C 130 TS 300

C 130 TS 200

C 120 TS 250

C 110 TS 300

C 110 TS 200

C 100 TS 245

C 90 TS 300

C 90 TS 200𝑦=0.0007𝑥2−0.1545𝑥+8.5712

56

It was also observed that there is a relation between average width standard deviation and deposition efficiency.

Figure 5.58 shows that the deposition efficiency behaviour is similar to average width standard deviation

behaviour.

Figure 5.58 – Relation between Average width standard deviation and deposition efficiency.

Following observation of figure 5.58, it was observed that the average width standard deviation is inversely

proportional to deposition efficiency. This can be explained by the following reasoning:

↑deposition efficiency Good fusion between layers i.e. ↓ width discrepancy between layers ↓

average width standard deviations

5.8.4.1. Average Width Standard Deviation Analysis on Design Expert

In this section the influence of current and travel speed on average width standard deviation predicted by DoE is

presented.

During this analysis, one of the steps to perform was find the model that best suits the response under study. For

each model is generated a different equation with n coefficients. Then, it was performed an analysis for

understand how significant is each coefficient, i.e., its influence on the response. After selecting the model the

final graph which represents the prediction of the influence of variable parameters on the response under study

was generated. For an optimal prediction, coefficients has to be significant, i.e., p-value less than 0.05. Values

above 0.1 are insignificant, i.e., doesn’t have influence on the response.

For this response, the significance of each coefficient of the most appropriate model for this response is shown

in figure 5.59.

0

20

40

60

80

100

0

0,5

1

1,5

2

80 90 100 110 120 130 140

Dep

osi

tion

Eff

icie

ncy

, [%

]

Aver

age

Wid

th S

tan

dar

d D

evia

tion

, [m

m]

Current, [A]

Average Width Standard Deviation and Deposition Efficiency

Average Width Standard Deviation TS 300 AverageWidth Standard Deviation TS 200Average Width Standard Deviation TS 250 Average Width Standard Deviation TS 300Average Width Standard Deviation TS 200 Average Width Standard Deviation TS 245Average Width Standard Deviation TS 300 Average Width Standard Deviation TS 200Deposition Efficiency TS 300 Deposition Efficiency TS 200Deposition Efficiency TS 250 Deposition Efficiency TS 300Deposition Efficiency TS 200 Deposition Efficiency TS 245

y=-0.014x2+3.183x-85.25

57

Figure 5.59 – Coefficients significance.

Figure 5.59 shows that all coefficients are insignificant, i.e., the prediction for the influence of current and travel

on the width standard deviation variation is not statistically correct. For this response is not possible with this

number of test get an optimal prediction of the influence of current and travel speed on width standard deviation

variation. However, it was opted to analyse the prediction obtained by this model, which is shown in figure 5.60.

Figure 5.60 – Influence of current and travel speed on width standard deviation.

In figure 5.60 the blue colour represents the lower width standard deviation area and the yellow colour

represents the higher width standard deviation area.

For this prediction the lowers width standard deviations occurs for travel speed of 200mm/min and 300mm/min,

and for currents between 110A and 125A.

Until a current of 110A and after a current of 130A the current has more influence on width standard deviation

than travel speed. Between these two values the travel speed has more influence than current.

Design-Expert® SoftwareFactor Coding: ActualThickness Standard Deviation (mm)

Design Points1.44

0.5


90 100 110 120 130

200

220

240

260

280

300

Thickness Standard Deviation (mm)

A: Current (A)

B: Tra

vel S

peed (m

m/m

in)

0.6

0.8

0.8

1

0.85

0.85

0.9

1.14

2

22

Average Width Standard Deviation (mm)

58

So to minimize the width standard deviation is need low or high travel speeds and currents between 110A and

125A.

5.8.5. Minimum Width

Based on the minimum width for each test, table 5-10, it was created a graph, figure 5.61, for a better

understanding of the results obtained.

Figure 5.61 – Current and travel speed influence on minimum width.

Figure 5.61 shows that an increase in current leads to an increase in minimum width. For travel speed, an

increase in travel speed leads to a decrease in minimum width.

So the minimum width is directly proportional to current and inversely proportional to travel speed.

To maximize the minimum width high currents and low travel speed must be used.

It is also important to point that the current has more influence in minimum width than the travel speed, proven

by the trend line slope of equation 23 and equation 24 for current and travel speed, obtained in excel for each


𝑦 = 0.0833𝑥 − 3.4875 (23)

𝑦 = −0.0186𝑥 + 11.24 (24)

The minimum width it is an important parameter once is in it that it’s based the machining process. It represents

the maximum width that can be achieved after machining the component.

5.8.6. Minimum Width Standard Deviation

Based on the minimum width standard deviation for each test, table 5.10, it was created a graph, figure 5.62, for

a better understanding of the results obtained.

0

2

4

6

8

10

80 90 100 110 120 130 140

Min

imum

Wid

th, [

mm

]

Current, [A]

Minimum Width

C 130 TS 300

C 130 TS 200

C 120 TS 250

C 110 TS 200

C 110 TS 300

C 100 TS 245

C 90 TS 300

C 90 TS 200

𝑦=0.0833𝑥−3.4875

59

Figure 5.62 – Current and travel speed influence in minimum width standard deviation.

For this analysis the point marked by the red ellipse, test 9 shown in figure 5.47, it was negligible due to its no

consistency with the two other tests which present the same variable parameters. The same occurs for the test

marked by the red square, test 1 figure 5.39.

Figure 5.62 shows that the minimum width standard deviation is inversely proportional with current.

5.9. Final Useful Area

Following average width and minimum width analysis the final useful area analysis was carried out. This

analysis consists on evaluate the build-up useful area percentage after a machining process and find a correlation

between final useful area and the variable parameters.



Figure 5.63 – Test 1 final useful area measurements.


0

0,5

1

1,5

2

2,5

80 100 120 140Min

imum

Wid

th S

tan

dar

d D

evia

tion

, [m

m]

Current, [A]

Minimum Width Standard Deviation

C 130 TS 300

C 130 TS 200

C 120 TS 250

C 110 TS 300

C 110 TS 200

C 100 TS 245

C 90 TS 200

C 90 TS 300

60



Figure 5.67 – Test 5 area final useful area measurements.




61






Table 5-13 shows the significant values for this analysis.

62

Table 5-13 – Summary table for final useful area analyses for experimental tests from Design Expert design.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Total removal area

required, [𝐦𝐦𝟐]

Final Area,

[𝐦𝐦𝟐]

Final Useful

Area, [%]

1 130 300 73.41 76.03 50.88

2 110 200 59.7 84 58.46

3 130 200 70.16 84.24 54.56

4 90 200 67.48 48.44 41.79

5 130 300 50.65 77.72 60.54

6 110 300 62.99 67.69 51.80

7 90 300 54.79 44.16 44.63

8 90 200 54.79 68.46 55.55

9 90 300 70.41 32.6 31.65

10 100 245 66.96 72.24 51.90

11 120 250 73.22 91.45 55.54

12 90 300 48.24 39 44.70

13 130 200 75.89 100.32 56.93

5.9.2. Experimental tests for DoE design validation




Table 5-14 shows the significant values for carried out DoE design validation.

Table 5-14 – Summary table for final useful area analyses for experimental test for Design Expert design validation.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Total removal area

required, [𝐦𝐦𝟐]

Final Area,

[𝐦𝐦𝟐]

Final Useful

Area, [%]

15 100 200 64.11 77.76 54.81

16 100 300 60.39 48.98 44.78

63

5.9.3. Final Useful Area

Based on the results obtained for final useful area for each test, table 5-13, it was created a graph, figure 5.78,

for a better understanding of the results obtained.

Figure 5.78 – Current and travel speed influence in final useful area.

For this analysis the point marked by the red circle, test 9 shown in figure 5.71, it was negligible due to its no

consistency with the two other tests which present the same variable parameters. The same occurs for the test

marked by the blue ellipse, test 1 figure 5.63.

Figure 5.78 shows that an increase in current leads to an increase in final useful area until a current of 110A and

a light decrease from the same value.

To travel speed, it was observed that and increase in travel speed leads to a decrease in final useful area.

So the final useful area percentage is directly proportional to current until a current of 110A and inversely

proportional from this value and inversely proportional to travel speed.

It is also important to point that the current has more influence in minimum width than the travel speed, proven

by the trend line slope of equation 25 and equation 26 for current and travel speed, obtained in excel for each


𝑦 = −0.014𝑥2 + 3.2802𝑥 − 141.54 (25)

𝑦 = −0.0666𝑥 + 71.78 (26)

The maximum final useful area was obtained for a test which had a current of 110A and a travel speed of

200mm/min.

It was also observed that there is a relation between final useful area and deposition efficiency. Figure 5.79

shows that the final useful area behaviour is similar, i.e., had the same evolution, to deposition efficiency. So an

increase in current until 110A leads to an increase in final useful area and in deposition efficiency, and a

decrease in final useful area and in deposition efficiency from this value. Thus, the final useful area is directly

proportional to deposition efficiency.

0

10

20

30

40

50

60

70

80

90

100

80 90 100 110 120 130 140

Fin

al U

sefu

l A

rea,

[%

]

Current, [A]

Final Useful Area

C 130 TS 300

C 130 TS 200

C 120 TS 250

C 110 TS 300

C 110 TS 200

C 100 TS 245

C 90 TS 300

C 90 TS 200

C

𝑦=−0.014𝑥2+3.2802𝑥−141.54

64

Figure 5.79 – Relation between Final Useful Area and Average Width Standard Deviation.´

Regarding to material usage efficiency, i.e. weight of the build-up after machining compared to the weight of

the build-up, expressed as a percentage. Its behaviour/evolution is the same that the one presented by final

useful area once is obtained by multiplication of the final area with two constants, shown in equation 27. So it is

directly proportional to deposition efficiency.

Material Usage Efficiency [%] =ρ × final Area × part lenght

Total Area (27)

5.9.3.1. Final Useful Area Analysis on Design Expert

In this section the influence of current and travel speed on final useful area, predicted by DoE is introduced.

For this response, the significance of each coefficient of the most appropriate model for this response is shown

in Figure 5.80.

0

20

40

60

80

100

0

20

40

60

80

100

80 90 100 110 120 130 140

Dep

osi

tion

Eff

icie

ncy

, [%

]

Fin

al U

sefu

l A

rea,

[%

]

Current, [A]

Final Useful Area vs Deposition Efficiency

Final Useful Area TS 300 Final Useful Area TS 200Final Useful Area TS 250 Final Useful Area TS 300Final Useful Area TS 200 Final Useful Area TS 245Final Useful Area TS 300 Final Useful Area TS 200Deposition Efficienccy TS 300 Deposition Efficiency TS 200Deposition Efficiency TS 250 Deposition Efficiency TS 300Deposition Efficiency TS 200 Deposition Efficiency TS 245Deposition Efficieny TS 300 Deposition Efficiency TS 200

𝑦=−0.014𝑥2+3.2802𝑥−141.54

y=-0.014x2+3.183x-85.25

65


Figure 5.80, shows that all coefficients are insignificant, i.e., the prediction of influence of the current and travel

speed on the average hardness variation isn’t statistically correct. It was observed that, for this response is not

possible with this number of test get an optimal prediction of the influence of current and travel speed on

thickness standard deviation variation.

However, it was opted to analyse the prediction obtained by this model, which is shown in figure 5.81.

Figure 5.81 – Influence of current and travel speed on final useful area.

Figure 5.81 shows that the highest useful area value is located around 120A and 130A. The orange colour

represents the highest final useful area percentage and the light blue colour represents the lesser final useful area

percentage.

It can be seen that the evolution of final useful area with current and travel speed is identical to the one

presented by deposition efficiency, figure 5.21, however slightly deviated to the current axis.

Design-Expert® SoftwareFactor Coding: ActualUseful area (%)

Design Points60.54

41.79


90 100 110 120 130

200

220

240

260

280

300

Useful area (%)

A: Current (A)

B: T

rave

l Spe

ed (m

m/m

in)

46

48

50 5254

56

58

2

2

66

Even knowing that the DoE prediction isn’t statically correct, its comparison with the experimental was carried

out.

The values obtained for final useful area for the tests for DoE design validation are shown on table 5-14. Table

5-15 shows the comparison between the final useful areas obtained by experimental and DoE.

Table 5-15 – Comparison between the final useful areas obtained by experimental and by DoE.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Experimental Final Useful

Area, [%]

DoE Final Useful

Area, [%]

15 100 200 54.81 [52, 54]

16 100 300 44.78 [50, 52]

Table 5-15 shows that the DoE prediction for final useful area is very near to reality.

5.10. Hardness

5.10.1. Experimental tests from DoE


Table 5-16 – Average Hardness analysis for experimental tests from Design Expert design.

Test Number Current, [A] Travel Speed, [mm/min] Average Hardness

1 130 300 201

2 110 200 185

3 130 200 202

4 90 200 185

5 130 300 198

6 110 300 191

7 90 300 186

8 90 200 190

9 90 300 190

10 100 245 178

11 120 250 186

12 90 300 181

13 130 200 178



67

Table 5-17 – Average Hardness analysis for experimental tests for Design Expert design validation.

Test Number Current, [A] Travel Speed, [mm/min] Average Hardness

15 100 200 186

16 100 300 177

Thereafter two images, figure 5.82, are presented which shows the indentation mark made during the hardness

test and the indentation marks along the sample after the hardness test.

Figure 5.82 – Indentation mark on the left and sample photo on the right.

5.10.3. Average Hardness

As can be seen is table 9-3 in appendix, there is no trend in the hardness variation along the build-up height and

the variation is insignificant. Figure 5.83 shows this observation.

Figure 5.83 – Hardness variation along build-up height

Thus it was opted to analyse the average hardness.

Following analysis of average hardness, table 5-16, it was observed that the difference between the higher value

and the lower value is not significant, which was about 20.

170

180

190

200

210

220

1 2 3 4 5 6 7 8 9 10 11

Har

dnes

s, H

V

Identation Number

Hardness, HV

Hardness, HV

68

5.10.3.1. Average Hardness Analysis on Design Expert

In this section the influence of current and travel speed on average hardness, predicted by DoE, is introduced,

figure 5.84.

Figure 5.84 – Influence of current and travel speed on average hardness.

Figure 5.84 shows that DoE predicted the same influence of current and travel speed on average hardness. As

can been seen in figure 5.84, the large part of the graphic area is dominated by the green colour confirming that

the average hardness variation with the current and travel speed is insignificant. Even so, it is important to note

that for high currents of 130A and for high travel speed of 300mm/min, the average hardness assumes the

highest value, and for low travel speed of 200mm/min, the average hardness assumes the lowest value. This can

be explained by the following reasoning:

C=const. and ↑TS ↓Heat Input ↑cooling rate ↑Hardness.

The values obtained for average hardness for the tests for DoE design validation are shown on table 5-17. Table

5-18 shows the comparison between the average hardness obtained by experimental and DoE.

Table 5-18 – Comparison between the average hardness’ obtained by experimental and DoE.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Experimental Average

Hardness, HV

DoE Average

Hardness, HV

15 100 200 186 ≈185

16 100 300 177 190

Table 5-18 shows that the DoE prediction for average hardness isn’t valid once the experimental result for test

16 isn’t in agreement with the result obtained by DoE.

Design-Expert® SoftwareFactor Coding: ActualAverage Hardness (HV)

Design Points201

178


90 100 110 120 130

200

220

240

260

280

300Average Hardness (HV)

A: Current (A)

B:

Tra

ve

l S

pe

ed

(m

m/m

in)

180

185

190

195

2

22

69

5.11. Surface Texture

From Alicona associate software several values of P-parameters was obtained which the most important for this

project are the average height of the profile standard deviation. It is presented the average of average height of

the profile standard deviation calculated for each side and the sample average of average height of the profile

standard deviation.

5.11.1. Experimental tests from DoE design


Table 5-19 – P profile parameters analysed and standard deviation averages for experimental test from Design Expert design.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Side Average profile

height Standard

Deviation, [mm]

Sample Average of Side Average

profile height Standard

Deviation, [mm]

1 130 300 A 0.2399

0.2913

B 0.3426

2 110 200 A 0.2862

0.3429 B 0.3995

3 130 200 A 0.4036

0.3426 B 0.2813

4 90 200 A 0.3838

0.3529 B 0.3219

5 130 300 A 0.2538

0.3171 B 0.3804

6 110 300 A 0.2010

0.2711 B 0.3412

7 90 300 A 0.2384

0.3255 B 0.4126

8 90 200 A 0.2087

0.1461 B 0.3756

9 90 300 A 0.2826

0.3547 B 0.4268

10 100 245 A 0.3606

0.3386 B 0.3165

11 120 250 A 0.2134

0.3094 B 0.4054

12 90 300 A 0.2842

0.2963 B 0.3083

13 130 200 A 0.2740

0.3411 B 0.4082

70

5.11.2. Experimental tests for DoE validation

In this chapter the measurements performed for experimental tests from DoE validation are presented.

Table 5-20 - P-profile parameters analysed and standard deviation averages for experimental experiments for Design Expert

validation.

Test

Number

Current,

[A]

Travel Speed,

[mm/min]

Side Average profile

height Standard

Deviation, [mm]

Sample Average of Side Average

profile height Standard

Deviation, [mm]

15 100 200 A 0.3959

0.4670 B 0.5380

16 100 300 A 0.2054

0.2679 B 0.3303

5.11.3. Average standard deviation and sample average standard deviation

In a first analysis of the results presented in the table 5-19, it was observed that there is a large discrepancy

between the average profile height standard deviation measured for each build-up side, and consequently,

between the side average profile height standard deviation.

The discrepancy between the values presented in table 5-19 can be originated by two different reasons, which

are a slight slope of the robot torch in relation to the work table perpendicular plane, i.e., have a different angle

of 90º with the work table perpendicular plane, and the magnetic arc blow.

When there is a slight slope of the robot torch, the arc is located more in one side than the other of the build-up,

leading to good fusion in one of the sides than the other. Thus, there is a higher roughness of one side than the

other one, and consequently a higher standard deviation in one of the sides than the other one.

On the other hand, this discrepancy between values can be also justified by the magnetic arc blow which is

caused by an unbalanced condition in the magnetic field surrounding the arc. In this phenomenon the arc is

deflected away from the normal arc path. It can deflect forward, backward, or sideways with respect to electrode

and welding direction, and consequently guide the melted material in the direction of the magnetic field. Thus,

the formed droplets are pushed / guided to the same side of the build-up leading to a build-up side more

irregular than the other. Therefore, there is a higher roughness on one of the sides than the other one and

consequently a greater standard deviation of one of the side than the other one.

In a second analysis, it was intended to study the influence of the variable parameters in the variation of the

sample average side average profile height standard deviation. Based on sample average side average standard

deviation for each test, table 5-19, it was created a graph, figure 5.85, for a better understanding of the results

obtained.

71

Figure 5.85 – Current and travel speed influence on sample average of side average standard deviation.

The test marked by the red ellipse in figure 5.85, it was negligible for this analysis because wasn’t consistent

with the trend of the other test.

Figure 5.85 didn’t show a clear influence of current and a travel speed on sample average of side average

standard deviation.

Then, it was considered evaluate this influence from the prediction obtained by DoE.

Analysing only the points represented by triangles, it’s suggests that this response is inversely proportional to

deposition efficiency.

5.11.3.1. Sample Average of Side Average Standard deviation on Design Expert

For this analysis was needed previously determinate the best value to consider in DoE. Among the average

standard deviation for each side and the sample average side average profile height standard deviation, was

chosen the highest side average standard deviation. The sample average side average profile height standard

deviation was negligible once was considered a rude approximation because the greater discrepancy between the

average standard deviation for each side. It was chosen between the side average profile height standard

deviation the highest standard deviation since conducts to a best prediction.

The significance of each coefficient of the most appropriate model for this response is shown in figure 5.86.


0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

80 90 100 110 120 130 140

Aver

age

Hei

gh

t of

the

pro

file

Sta

nd

ard

Dev

iati

on

, [m

m]

Current, [A]

Sample Average of side Average Standard

Deviation

TS 300

TS 200

TS 250

TS 300

TS 200

TS 245

TS 300

TS 200

72

Figure 5.86 shows that all coefficients are insignificant, i.e., the prediction for the influence of current and travel

speed on the side average profile height standard deviation isn’t statistically correct. So for this response is not

possible with this number of tests get an optimal prediction. However, it was opted to analyse the prediction

obtained by this model, which is shown in figure 5.87.

Figure 5.87 – Influence of current and travel speed on surface standard deviation.

In figure 5.87 the light blue colour represents the lowest side average standard deviation area and the red colour

represents the highest side average standard deviation area.

The lowest average profile height standard deviation occurs for high travel speed of 300mm/min and for high

currents of 130A. In turn, the highest side average profile height standard deviation occurs for low travel speed

of 200mm/min and high currents of 130A.

Once it was observed any influence of the variable parameters in the sample average of side average profile

height standard deviation, it was considered to perform a second analysis which relates the height profile range

variation for each side with the final useful area.

From figure 5.87 to figure 5.99, it is shows the build-up surface distance variations to the surface roughness

measurement average plan, for each side of each test from DoE.

Design-Expert® SoftwareFactor Coding: ActualSurface stand dev average (mm)

Design Points0.4126

0.3412


90 100 110 120 130

200

220

240

260

280

300

Surface stand dev average (mm)

A: Current (A)

B: T

ravel S

peed (

mm

/min

)

0.36

0.37

0.39

0.39

0.4

0.41

0.388

0.388

0.38

2

2

73

Figure 5.88 – Build-up surface distance variations to the surface roughness

measurement average plan for test 1. Top: face A, Bottom: face B.







74









75









76

Figure 5.100 – Build-up surface distance variations to the surface roughness measurement average plan for test 13. Left: face A,

Right: face B.

In order to facilitate the analysis of these parameters, it was created table 5-21 which presented the whole

significant values.

Table 5-21 – Height´s profile range variation of each test for each side.

Test Number Current, [A] Travel Speed,

[mm/min]

Side A variation

range, [mm]

Side B variation

range[mm]

1 130 300 1.8 2.4

2 110 200 1.3 2.4

3 130 200 2.6 2.2

4 90 200 2.2 2.4

5 130 300 1.4 2.6

6 110 300 1.4 2.2

7 90 300 2 1.6

8 90 200 1.2 1.6

9 90 300 1.8 2.2

10 100 245 3 2.2

11 120 250 1.6 3

12 90 300 1.6 1.8

13 130 200 2.4 2.4

Based on table 5-16, it was created the graph shown in figure 5.102, which presented the relation between each

side height´s profile variation range with the corresponding final useful area.

77

Figure 5.101 – Relation between sides’ height profile and final useful area.

Following observation of figure 5.101, it was observed that there is no relation between side´s height profile and

the final useful area, once it was obtained a high final useful area for a test with a high side´s height profile

variation range, test 13, as for a test width low height profile, test 8.

5.12. Optimization on Design Expert software

5.12.1. Introduction

In this section the results obtained for the optimization phase are present. It was performed a numerical

optimization which is explained in detail.

5.12.1. Numerical Optimization

In this phase, the intended purpose of each response was selected, i.e., if it is want to maximize or minimize

each response. According to the objective of this work, it was decided to minimize the sample average standard

deviation, minimize the width standard deviation, maximize the final useful area percentage, maximize the

deposition rate and maximize the deposition efficiency.

The responses missing, average thickness and average hardness, were discarded since haven’t great influence on

the objective of this work. The optimal situation is the intended point has a desirability of 1.

The result for the optimization is presented in figure 5.102.

0

10

20

30

40

50

60

70

0

0,5

1

1,5

2

2,5

3

3,5

1 2 3 4 5 6 7 8 9 10 11 12 13

Fin

al U

sefu

l A

rea,

[%

]

Var

iati

on R

ange,

[m

m]

Test Number

Relation between Sides' height profile and Final Useful

Area

Side A Side B Final Useful Area

78

Figure 5.102 – Optimization graph.

Based on the analysis of each response for the variable parameters ranges, the optimal point provided by Design

Expert is illustrated in figure 5.102. This point is characterized by a current of 124.6A and a travel speed of 300

mm/min. It is also observed that this point had a desirability of 0.724. The numerical optimization was

performed using the whole analysis, whole with the same weight. This explains the desirability of 0.724

obtained, because some analyses are insignificant and has the same importance/weight as the others, which are

significant.

5.13. Summary

In this chapter the results were presented and discussed in great detail.

Design-Expert® SoftwareFactor Coding: ActualDesirability

Design Points1

0


90 100 110 120 130

200

225

250

275

300

Desirability

A: Current (A)

B: Tra

vel S

peed (m

m/m

in)

0.2

0.2

0.364218 0.491363

0.491363

0.587117

0.587117

0.553763

0.553763

0.617516

0.617516

2

2

Prediction 0.723991X1 124.633X2 300

79

6. Conclusions

Additive manufacturing of Nickel components using CMT process has been developed showing promise for

automotive and aerospace industries. The whole proposal objectives for this project were reached and the main

conclusions of the build-ups carried out in this work to develop this process are:

1. To obtain a build-up layer the minimum current used was 90A. For lower values no homogenous layer

was achieved welding speeds between 200 and 300 mm/min.

2. The first layer a different set of parameters must be used to avoid too small widths in the first layer,

high current and low travel speed.

3. For relatively low currents of 90A and for relatively high travel speeds of 300mm/min, disbonding

defect occurs.

4. The maximum deposition rate, 2.55Kg/h, occurs for a test with a current of 130A and a travel speed of

200mm/min. This response is directly proportional to current and inversely proportional to travel

speed. Current has a higher influence on deposition efficiency than travel speed.

5. The maximum deposition efficiency, 96%, occurs for a test with a current of 110A and a travel speed

of 200mm/min. The deposition efficiency is directly proportional to the current until a value of 110A

and inversely proportional from this value. Regarding to travel speed, deposition efficiency is inversely

proportional to travel speed. It was also concluded that the current has more influence in deposition rate

than the travel speed.

6. The poor fusion effect is directly proportional to the travel speed and inversely proportional to current.

Accordingly to maximize this parameter must be used high currents and low speed travel.

7. Average width is directly proportional to current and inversely proportional to travel speed. For this

parameter, the maximum value obtained, 9mm, occurs for a test with a current of 130A and a travel

speed of 200mm/min.

8. Average width standard deviation is inversely proportional to deposition efficiency.

9. Minimum width is directly proportional to current and inversely proportional to travel speed. This

parameter has the same behaviour that the average width according to current and travel speed.

10. Minimum width standard deviation is inversely proportional to current.

11. The final useful area percentage is directly proportional to current until a current of 110A and inversely

proportional from this value and inversely proportional to travel speed. It is influenced by deposition

efficiency. The maximum final useful area occurs for a test which has a current of 110A and a travel

speed of 200mm/min.

12. High current and low travel speed implies high heat input that leads to good fusion between layers.

High good fusion between layers implies high final useful area percentage

13. The influence of the variable parameters, current and travel speed, on average hardness is insignificant

for the range analysed.

14. It was concluded any influence of the variable parameters in the sample average of side average

standard deviation.

15. It was conclude that there is no relation between side´s height profile and final useful area.

80

16. The optimal point provided by Design expert had a desirability of 0.724, is characterized by a current

of 124.6A and a travel speed of 300mm/min.

7. Recommendations

Although the multi-layer build-ups performed by CMT process have been developed by the current work, more

process development work is required before application. This includes:

Further optimization of welding parameters, current and travel speed, based on the optimum areas

obtained during this work.

Design, build and test different shapes performed by this process.

Application of the process to materials of other potential interest.

81

8. References

Adebayo. A, Mehnen, J. & Tonnellier, X., 2013. Limiting Travel Speed in Additive Layer Manufacturing.

Trends in Welding Research: Proceedings of the 9th International Conference, 3, pp.1038–1044.

Alberti, E.A., Silva, L.J. Da & d’Oliveira, A.S.C.M., 2014. Manufatura Aditiva: o papel da soldagem nesta

janela de oportunidade. Soldagem & Inspeção, 19, pp.190–198.

Baker, R. (1925), Method of Making Decorative Articles, US Patent 1,533,300, filed Nov. 12 1920, patented

14th April 1925

Choi, J. & Chang, Y., 2005. Characteristics of laser aided direct metal/material deposition process for tool steel.

International Journal of Machine Tools and Manufacture, 45, pp.597–607.

Clark, D., Bache, M.R. & Whittaker, M.T., 2008. Shaped metal deposition of a nickel alloy for aero engine

applications. Journal of Materials Processing Technology, 203, pp.439–448.

Colegrove, P. a. et al., 2013. Microstructure and residual stress improvement in wire and arc additively

manufactured parts through high-pressure rolling. Journal of Materials Processing Technology, 213(10),

pp.1782–1791.

Dinda, G.P., Dasgupta, a. K. & Mazumder, J., 2009. Laser aided direct metal deposition of Inconel 625

superalloy: Microstructural evolution and thermal stability. Materials Science and Engineering A, 509,

pp.98–104.

Ding, D. et al., 2015. Robotics and Computer-Integrated Manufacturing A multi-bead overlapping model for

robotic wire and arc additive manufacturing ( WAAM ). Robotics and Computer Integrated

Manufacturing, 31, pp.101–110.

Dutra, J.C., Gonçalves e Silva, R.H. & Marques, C., 2013. Características de fusão e potência de soldagem com

a transferência MIG - CMT versus MIG convencional para alumínio 5183. Soldagem e Inspecao, 18,

pp.12–18.

Frazier, W.E., 2014. Metal additive manufacturing: A review. Journal of Materials Engineering and

Performance, 23(June), pp.1917–1928.

Fronius. CMT: Cold Metal Transfer. Technical Report, Austria: Fronius International GmbH, 2005.

Hopkinson N (2012), Additive Manufacturing: Technology and Applications, British Educational

Communications and Technology Agency (BECTA)

82

Hudson, M. G. (2004), Welding of X100 Linepipe, Cranfield University, Cranfield Press

J.F.Song , T.V.Vorburguer, National Institute of Standards and Technology

Kang, J. G., Ryu, G. S., Kim, D. C., Kang, M. J., Park, Y. W., & Rhee, S. (2013). Optimization of arc-start performance by wire-feeding control for GMA welding. Journal of Mechanical Science and Technology,

27(2), 501–509. doi:10.1007/s12206-012-1240-7

Karunakaran, K.P. et al., 2010. Low cost integration of additive and subtractive processes for hybrid layered

manufacturing. Robotics and Computer-Integrated Manufacturing, 26(5), pp.490–499.

Kruth, J.-P., Leu, M.C. & Nakagawa, T., 1998. Progress in Additive Manufacturing and Rapid Prototyping.

CIRP Annals - Manufacturing Technology, 47(I), pp.525–540.

Levy, G.N., Schindel, R. & Kruth, J.P., 2003. Rapid Manufacturing and Rapid Tooling With Layer

Manufacturing (Lm) Technologies, State of the Art and Future Perspectives. CIRP Annals -

Manufacturing Technology, 52(Lm), pp.589–609.

Lorant, E. (2010), Effect of microstructure on mechanical properties of Ti-6Al-4V structures made by Additive

Layer Manufacturing , Phd thesis, Cranfield University, Cranfield.

Mehnen, J. et al., 2010. Design for wire and arc additive layer manufacture. , pp.19–21.

Palani, P. K., & Murugan, N. (2006). Selection of parameters of pulsed current gas metal arc welding. Journal of

Materials Processing Technology, 172, 1–10. doi:10.1016/j.jmatprotec.2005.07.013

Pickin, C.G., Williams, S.W, M.L., 2011. Characterisation of the cold metal transfer (CMT) process and its

application for low dilution cladding. , 211(3), pp.496–502.

Raj, Baldev; Jayakumar, T.; Thavasimuthu, M. ,2002, Practical non-destructive testing (2nd ed.), Woodhead

Publishing, ISBN 978-1-85573-600-9.

De Resende, A.A. et al., 2009. Influência das correntes de soldagem do processo plasma-MIG sobre a geometria

do cordão de solda e taxa defusão do arame. Soldagem e Inspecao, 14(5), pp.320–328.

Rombouts, M. et al., 2006. Fundamentals of selective laser melting of alloyed steel powders. CIRP Annals -

Manufacturing Technology, 55(1), pp.187–192.

Santos, J. Oliveira e Quintino, Luísa. Processos de Soldadura. Edições Técnicas do Instituto de Soldadura e

Qualidade, 1993. ISBN 972-9228-17-5.

http://books.google.com/books?id=qXcCKsL2IMUC

http://en.wikipedia.org/wiki/International_Standard_Book_Number

http://en.wikipedia.org/wiki/Special:BookSources/978-1-85573-600-9

83

Wilson, I.L.W. et al., 1991. the Effect of Heat Input on Microstructure and Cracking in Alloy 625 Weld

Overlays. Superalloys 718, 625 and Various Derivatives, pp.735–747.

Wohlers, T. (2010), Additive manufacturing state of the industry, in Wohlers associates, USA.

Wohlers, T. (2011), Wohlers report, Additive manufacturing state of the industry, Annual worldwide progress

report, Wohlers associates, USA.

Wohlers, T. & Gornet, T., 2011. History of additive manufacturing Introduction of non-SL systems Introduction

of low-cost 3D printers. Wohlers Report 2011, pp.1–23.

Wong, K. V. & Hernandez, A., 2012. A Review of Additive Manufacturing. ISRN Mechanical Engineering,

2012, pp.1–10.

Yan, X. & Gu, P., 1996. A review of rapid prototyping technologies and systems. CAD Computer Aided

Design, 28(4), pp.307–318.

Sites

[1] http://www.lincolnelectric.com/en-gb/support/process-and-theory/Pages/aluminum-feeding-detail.aspx,

accessed on the 20th October, 2014.

[2] http://www.weldreality.com/STEELS%20NICKEL%20ALLOYS.htm, accessed on the 20th October ,

2014.

[3] http://www.californiametal.com/Inconel_625_Sheet_Plate_Pipe_Tube_Rod_Bar_Tech_Data.htm, accessed

on 10th April 2015.

General References

Adebayo, A., 2013. Characterisation of Integrated WAAM and Machining Processes. Cranfield University.

Ding, J., 2012. Thermo-mechanical Analysis of Wire and Arc Additive Manufacturing Process. Cranfield

University.

Jianing Guo, 2012. Feature Based Cost and Carbon Emission Modelling for Wire and Arc Additive

Manufacturing. Cranfield University.

Pépe, N.V. da C., 2010. Advanves in Gas Metal Arc Welding and Application to Corrosion Resistant Alloy

Pipes. Cranfield University.

http://www.lincolnelectric.com/en-gb/support/process-and-theory/Pages/aluminum-feeding-detail.aspx

http://www.weldreality.com/STEELS%20NICKEL%20ALLOYS.htm

http://www.californiametal.com/Inconel_625_Sheet_Plate_Pipe_Tube_Rod_Bar_Tech_Data.htm

84

Simões. Thiago Mesquita, 2014. Curvas S-N da Camada de Inconel 625 Depositada por Soldagem em Tudos

Cladeados. Universidade Federal do Rio de Janeiro.

Zhai, Y., 2012. Early Cost Estimation for Additive Manufacture. Cranfield University.

85

9. Appendix

Table 9-1 – Whole data needed to deposition rate and deposition efficiency analysis for experimental tests from Design

Expert design.

Test

Number

Deposition

rate, [Kg/h]

Length of wire

used, [cm]

Volume of wire

used, [𝐜𝐦𝟑]

Weight of

Electrode used,

[Kg]

Deposition

Efficiency, [%]

1 2.51 2263.2 17.78 0.150 91.32

2 2.23 2139.04 16.80 0.142 96.62

3 1.95 2484 19.51 0.165 71.06

4 1.63 1938.6 15.23 0.129 91.05

5 2.42 2005.14 15.75 0.133 88.03

6 2.11 1914.93 15.04 0.127 91.38

7 1.41 1471.5 11.56 0.098 78.94

8 1.65 1919.7 15.08 0.127 91.94

9 1.41 1476 11.59 0.098 78.70

10 1.64 1804.64 14.17 0.120 81.09

11 2.37 2579.2 20.26 0.171 91.83

12 1.58 1317.15 10.34 0.087 88.19

13 2.55 2553 20.05 0.169 92.77

Table 9-2 – Whole data needed to deposition rate and deposition efficiency analysis for experimental tests for Design Expert

validation.

Test

Number

Deposition

rate, [Kg/h]

Length of wire

used, [cm]

Volume of wire

used, [𝐜𝐦𝟑]

Weight of

Electrode used,

[Kg]

Deposition

Efficiency, [%]

15 1.93 2177.7 17.10 0.144 94.91

16 1.82 1632 12.82 0.108 89.66

86

Table 9-3 – Whole data needed to average hardness analysis for experimental tests from Design Expert design

Test

Number Data

Indentation Number

Weld Substrate

1 2 3 4 5 6 7 8 9 10 11 12

1

d1 218 209 207 216 215 215 215 212 209 206 205 206

d2 227 216 215 219 219 222 217 218 216 213 215 207

HV 187 205 208 196 196 194 199 200 205 211 211 217

Average

HV 201

2

d1 209 214 223 219 223 225 235 226 230 222 206 223

d2 221 216 225 238 228 228 244 228 233 225 220 225

HV 201 201 185 178 182 180 162 180 173 186 204 185

Average

HV 185

3

d1 215 219 221 208 215 217 211 213 212 204 198 196

d2 217 221 226 214 216 219 214 221 219 217 199 197

HV 198 192 185 208 200 195 205 197 200 210 235 240

Average

HV 202

4

d1 219 207 214 228 215 232 216 255 215 210 228 227

d2 228 214 223 228 228 240 225 261 221 211 239 228

HV 186 209 194 179 189 167 191 140 195 209 171 180

Average

HV 185

5

d1 213 218 216 215 215 216 219 213 212 212 208 218.5

d2 220 225 216 225 216 223 219 214 214 220 210 218.8

HV 198 189 199 192 200 192 194 203 204 199 212 194

Average

HV 198

6

d1 211 216 216 228 214 218 222 215 222 218 208 220

d2 211 220 221 234 223 222 224 220 233 236 215 222

HV 208 195 194 174 194 191 186 196 179 180 207 189

Average

HV 191

7

d1 223 214 221 217 212 243 220 215 228 224 214 231.7

d2 224 225 225 225 219 250 224 221 230 235 216 231

HV 185 192 186 190 200 153 188 195 177 176 201 173

Average

HV 186

87

8

d1 212 221 225 218 216 221 221 219 217 216 206 222

d2 223 224 226 226 232 228 225 223 230 222 211 223

HV 195 188 182 188 184 184 186 190 185 190 213 188

Average

HV 190

9

d1 211 214 221 218 224 223 211 208 213 220 225 230

d2 216 220 223 244 228 234 228 215 223 225 229 233

HV 203 197 188 174 182 177 192 207 195 188 180 173

Average

HV 190

10

d1 222 222 223 225 222 273 227 225 221 218 195 228

d2 242 237 239 240 227 281 230 230 239 220 201 234

HV 172 176 173 172 184 121 177 179 175 194 236 174

Average

HV 178

11

d1 216 219 219 221 232 223 225 217 218 213 230 217

d2 221 224 223 228 232 225 235 222 220 220 232 223

HV 195 189 190 184 172 185 175 192 194 198 174 191

Average

HV 186

12

d1 218 223 227 223 225 224 215 225 219 221 217 232

d2 240 240 234 230 234 228 223 232 236 231 218 235

HV 177 173 175 181 176 181 194 177 179 182 196 171

Average

HV 181

13

d1 221 228 228 209 233 233 239 221 224 226 218 229

d2 221 232 236 212 241 234 250 236 229 229 226 230

HV 190 175 173 210 165 170 155 177 181 179 188 176

Average

HV 178

88

Table 9-4 – Whole data needed to average hardness analysis for experimental tests for Design Expert validation.

Table 9-5 - Whole data needed to surface texture for experimental tests from Design Expert design.

Test

Number

Face Measurement

number

Average height

of profile, Pa,

[um]

Standard

Deviation,

[mm]

Side

Average of

Standard

Deviation,

[mm]

Sample Average of

side average

Standard

Deviation, [mm]

1

A

1 229.0451 0.2859 0.2399

0.2913

2 128.5922 0.1688

3 200.2888 0.2651

B

1 215.3467 0.2987 0.3426

2 223.1935 0.2791

3 325.1192 0.4501

2

A

1 220.0389 0.2855 0.2862

0.3429

2 206.3944 0.3001

3 210.2436 0.2732

B

1 357.5817 0.4367 0.3995

2 204.6795 0.3467

3 151.1401 0.4153

3

A

1 314.7889 0.3982 0.4036

0.3426

2 442.8586 0.5634

3 183.5957 0.2493

B 1 190.4391 0.2913

0.2813 2 238.4789 0.2917

Test

Number Data

Indentation Number

Weld Substrate

1 2 3 4 5 6 7 8 9 10 11 12

15

d1 202 219 239 229 222 218 212 222 227 213 205 228

d2 240 221 243 232 229 231 224 250 229 217 205 230

HV 190 191 160 175 182 184 195 167 179 201 221 176

Average

HV 186

16

d1 208 246 228 215 226 228 232 223 219 221 214 234

d2 242 250 232 224 235 248 241 236 230 231 213 235

HV 183 151 175 192 174 163 166 176 184 182 203 169

Average

HV 177

89

3 157.2719 0.2610

4

A

1 365.2114 0.4280 0.3838

0.3529

2 217.2717 0.3102

3 253.2548 0.4133

B

1 265.8164 0.3278 0.3219

2 291.8194 0.3827

3 207.5491 0.2553

5

A

1 153.7244 0.1807 0.2538

0.3171

2 179.2304 0.2931

3 177.0780 0.2878

B

1 227.6505 0.2936 0.3804

2 126.0387 0.5606

3 214.9657 0.2872

6

A

1 180.0694 0.2326 0.2010

0.2711

2 138.6732 0.1747

3 151.8925 0.1959

B

1 250.7876 0.3620 0.3412

2 162.0937 0.2527

3 211.7311 0.4091

7

A

1 254.9255 0.3251 0.2384

0.3255

2 149.1923 0.2196

3 144.9446 0.1707

B

1 351.6914 0.4122 0.4126

2 401.6311 0.4734

3 295.9631 0.3524

8

A

1 172.7199 0.2253 0.2087

0.1461

2 145.5567 0.2012

3 159.7004 0.1996

B

1 301.4706 0.3643 0.3756

2 339.0864 0.4164

3 283.7295 0.3463

9

A

1 167.1603 0.3077 0.2826

0.3547

2 169.4425 0.2871

3 201.3170 0.2530

B

1 517.0840 0.5867 0.4268

2 217.7809 0.2789

3 322.4554 0.4148

10 A

1 306.0072 0.3760 0.3606

0.3386 2 289.4433 0.3847

3 256.1415 0.3211

90

B

1 294.1108 0.3531 0.3165

2 98.1219 0.1501

3 194.6508 0.4463

11

A

1 142.0734 0.1681 0.2134

0.3094

2 174.9274 0.2869

3 137.7984 0.1852

B

1 289.3109 0.3960 0.4054

2 147.5953 0.4126

3 235.1504 0.4078

12

A

1 126.8940 0.3476 0.2842

0.2963

2 176.2542 0.2753

3 108.8193 0.2297

B

1 304.3287 0.3675 0.3083

2 174.1661 0.2110

3 269.5481 0.3465

13

A

1 196.7611 0.3384 0.2740

0.3411

2 123.0195 0.2601

3 160.2039 0.2236

B

1 251.1859 0.3508 0.4082

2 209.8259 0.3366

3 423.8277 0.5374

Table 9-6 – Whole data needed to surface texture analysis for experimental tests for Design Expert validation

Test

Number Face

Measurement

number

Average height

of profile, Pa,

[um]

Standard

Deviation,

[mm]

Average

Standard

Deviation, [mm]

Sample Average

Standard

Deviation, [mm]

15

A

1 260.3418 0.3790 0.3959

0.4670

2 231.8422 0.3534

3 292.3854 0.4553

B

1 378.6510 0.5107 0.5380

2 379.2507 0.5352

3 324.7609 0.5682

6

A

1 166.9682 0.2107

0.2054

0.2679

2 167.6250 0.2008

3 167.6120 0.2048

B

1 216.3268 0.2681

0.3303 2 257.0169 0.3590

3 286.3952 0.3637

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