Department of Machinery

Cowork with Georgia Tech

Oli tehtud mitmed artiklid koos.2 doktoranti külastasid Yan Wang-i, kuid siis kui ta oli UCF ülikoolis.Yan Wang on välis liige meie grandil ETF9460Shevtshemko tegi Yan Wangiga koos projekti Fulbright raames VF446.Rohkem infot koostöö osas saab vaadata www.idssteam.com

Cowork with Georgia Tech

1. Shevtshenko, E.; Bashkite, V; Maleki, M.; Wang, Y. (2012). Sustainable Design of Material Handling Equipment: A win-win approach for manufacturers and customers. Mechanika, 18(5)

2. Sahno, J.; Opik, R.; Kostina, M.; Paavel, M.; Shevtshenko, E., Wang, Y. (2012). Knowledge Management Framework for Production Route Selection in Manufacturing Enterprises. In: Proceedings of the 8th International Conference od DAAAM Baltic Industrial Engineering 19-21st April 2012

3. Shevtshenko, E.; Yan, W. (2009). Decision support under uncertainties based on robust Bayesian networks in reverse logistics management. International Journal of Computer Applications in Technology, 36

4. Shevtshenko, E.; Karaulova, T.; Kramarenko, S.; Wang, Y. (2009). Manufacturing project management in the conglomerate enterprises supported by IDSS. Journal of Achievements in Materials and Manufacturing Engineering, 33(1)

5. Shevtshenko, E.; Karaulova, T.; Kramarenko, S.; Wang, Y. (2009). Manufacturing project management in the conglomerate enterprises supported by IDSS. Journal of Achievements in Materials and Manufacturing Engineering, 33(1), 94 - 102.

• Shevtshenko, E., Zahharov, R., Karaulova, T.; Wang, Y. (2008). Advanced Concepts Integration for the Compression of Construction Project Schedule. Katalinic, B. (Toim.). DAAAM International Scientific Book 2008 (759 - 772). Viin, Austria: DAAAM International Vienna

• Kramerenko, S.; Shevtshenko, E.; Karaulova, T.; Wang, Y (2008). Decision Analysis in Project Management Process. Journal of the Machine Engineering, 8(2)

• Shevtshenko, E.; Karaulova, T.; Kramarenko, S., Y. Wang (2007). IDSS used as a framework for collaborative projects in conglomerate enterprises. Journal of Achievements in Materials and Manufacturing Engineering, 22.

• Shevtshenko, E.; Karaulova, T.; Kramerenko, S.; Wang, Y. (2007). IDSS as a tool for project management in a collaborative network of SME-S. Journal of the Machine Engineering, 7(2, Manufacturing Intelligent Design and Optimization)-

Common Structure of Mektory Project Implementation

MEKTORY Project

Enterprise diagnostic

Reconstruction of the manufacturing system

Contact with enterpriseImprovements suggestions

Processes modelling and simulation

Reliability analysis of the processes

Masters & Doctoral students, researches

Case study for thesis

Contact with other

universitats

Tasks of Project

1. Choosing of the most appropriate methods for analysis at machinery enterprises

2. Elaboration of the effective decision making methodology for production process reliability growth

3. Connection of the methodology with the standard methods for reliability estimation

4. Practical implementation of the proposed methodology5. Transfer of data from analyzing system to decision making

system

Objective is increasing of efficiency and productivity of the company.

Using the new methods and elaboration new tools for enterprise efficiency increasing.

Project for Densel Baltic•The main aim of the current project is Database elaboration for ISO 9001 documentation for SME•The database allows quickly determine relationship between different ISO documents and get constant feedback of the company’s quality system.

view of the infosystem development

Main procedures

Additional procedures

Database structure and implementation

Procedure description

Structure of procedure

implemented in ARIS

NPV (Net present value) calculation procedure

Implementad in MS EXCEL

Ü

Document connected with current procedure

Processes reliability assesment

Reliability assessment tool must help engineers quickly and with

great precision estimate the most unreliable places of a production

process and to suggest the most efficient ways for reliability

improvement

Process Model is Base for Reliability Analysis

SMEs encounter with difficulties to implement reliability principles in production due to:• complexity of existing methods for reliability estimation• necessity of expensive software and skilled employees• difficulties in decision making for reliability improvement

Common framework of research

Quantitative measures of system reliability

Recommendation for reliability improvement

Enterprise statistical data of faults

Process model

FTA

Reliability prediction

RBD

Equipment maintenance plan

Input data

Methods

Results

BBN for decision making

Classifier of faults

FMEA

Extended part of reliability analysisMain part of reliability analysis

Reliability of manufacturing process

Data transfer from FMEA to BBN

Faults classifier development

Grouping of faults in FMEA by codes

FMEA creation using faults codes from classifier

Calculating of faults probability for every failure class

Calculating of faults probability for every failure cause code

Transferring data from Excel to BBN system

Connecting of nodes in BBN

GE

NE

RA

L C

alc

ulat

ing

too

l B

BN

FMEA

Selection from FMEA

Failureclass

Cause code

Sum RPN

Failure probability

2 2A 92 0,041

2 2D 96 0,043

3 3A 193 0,086

Template of the classifier of faults in BBN

The algorithm of integration process

Creation of classifier structure in BBN

Generation of a code of the sucture

Matching of the generated code with the failure causes codes from Excel table

Cause code is found

Yes No

Marks this box with another colour

Put probability of faults from Excel table to right place

Deletes marked boxes

New structure creation in BBN system from finished file

Inclusion of the corrective actions into BBN and testing of their influence on the whole

process

Skip to the next string

BBN after “Poke-Yoke” implementation

Probability of error on the top level is 11%!It was improved by 3%. Probabilities of “Personnel error” class and “Inattention to details” were improved by 3% and 16%, respectively