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© WZL/Fraunhofer IPT

Industrie 4.0 and Smart ServiceBig Data, small Data, New Business ModelsState of the Art and Room for Joint Research

Professor Fritz Klocke Chair of Manufacturing Technology Werkzeugmaschinenlabor WZL - RWTH Aachen University Head of the Fraunhofer Institute for Production Technology IPT

Tokyo Institute of Technology - RWTH Aachen University

Aachen June 12th, 2015

Seite 2© WZL/Fraunhofer IPT

Outline

�Introduction

�Big Data

�Physical Modelling, multiscale Modelling

�Process chains and Uncertainty

�Reduced Models, Assistance Systems

�Model Validation and Pilot-Plants

�Conclusion


Smart Factories –Characteristics and Perspectives

Single Source of Truth IT-GlobalizationPLM/Engineering-

systems�Big data�Assessing and storage in cloud�Data mining, safety, security�High speed computing

ERP-systems

cyber

physical

SoftwareHardware

�Adaptation by sensors�Intuitively, reliability�IT-Openness�Cost-efficiency�Robustness Automation

�Businesscommunities

�Socialcommunities

Productivityand Economic

Efficiency through

Collaboration

Localdata storage

Cooperation

Cognitivesystem


Smart Services

Quellen: Samsung AG; Medtronic Ltd.; Claas KGaA mbH; Joy Global Inc.; Tesla Motors; Dr Vortrag D. Heeschen (2015)


Smart Services

Smart Services

� …statistics

� …filtering and compacting of data

� …provide information in a user-friendly way

� …increase efficiency

� …new business models

Smart Services

User User

AnwenderAnwenderSmart ProductsSmart MachinesSmart ProductsSmart Machines

Smart Services

Quelle: Smart Service Welt – BMWi, Dr Vortrag D. Heeschen (2015)


Outline

�Introduction

�Big Data

�Sensors

�Modelling

�Uncertainty



�Conclusion


BIG DATA Die 3V Definition nach Gartner IT Inc.

Big DataBig

Data

VolumeVolume

VarietyVarietySpeedSpeed

DataDiversity

Data Volume

DataTransmission

Quelle: Gartner IT Inc.

If one dimension is fare above state of the art:

Big data


CombinationsDifferent information sources for knowledge acquisi tion

� Data of better quality

� Reliability and accuracy improvement

� egrains

� Virtual sensors

Sensor+

Sensor

� Combination of recorded data and human experience

� Field information

� State variables

Sensor+

Human

Sensor+

Model

� Model calibration

� Model based Process Control

Model+

Human

� Parameter setting with the help of human experience and models

� Technology Apps

� Men machine interface

Source: Kistler, FLIR


Variety / Diversity

Quelle: Facebook.com, Twitter.com, Adobe, geography.ryerson.ca, HiSoftware.com, Microsoft, psdgrafics.com, camtex.com, manufacturingscience.com, enzyklopaedie-der-wirtschaftsinformatik.de

Social Internet Production


Material Flow

• RFID, RTLS, Barcodes, e-grains (autarc radiation sensors, knot networks)

• Identification and Localization von Material carrier Transparency and real time information

• Synchronization of Material Flow and Information Flow

• „smart reusable transport items“ (smaRTI) imEffizienzcluster LogistikRuhr

• Under Test in consumer good industry: CHEP, MARS und REWE

Self – controlled Logistic

© minicel73 - Fotolia.com

´Source: Fraunhofer IML, Fraunhofer IZM, Fraunhofer-Allianz Big Data


Trend-Observation

�Social-Media-Monitoring im Living Lab

�Bsp.: MOTOR‐TALK.de is the biggestOnline- Community for automotive in Germany

Objective

Search 35 Million Blog-postings

Find emotional impacts – from happiness to annoyance

Living Lab – Real time classification of new entry items

Trend analyses´Source: Fraunhofer-Allianz Big Data


Data sharing creates added value – smart serviceJoint Data base – service provider – smart service

1

2

Identification of core knowledge

3 Automatic data assessment

Commercialization of knowledge

4

Data acquisition

Best Practices Tool and Die Academy Aachen

Joint Industrial Consortium> 70 companies partnering

Other market participants

Marketplace Technology Data

Machine tools

Providers of raw materials

Technology data

End Users

Technology data

Technology data

Source: Werkzeugbauakademie - WBA, Aachen

Tool provider

Technology data


Outline

�Introduction

�Big Data


�Uncertainty



�Conclusion


Multiphysics Modelling – big DATA, high-speed comput ing

Surface Reactions

Fluid Mechanics

GeometryStructure

Heat Transfer

Electric Field

Time scale

Leng

th s

cale

Pro

cess

Cry

stal

/ S

truc

ture

Ato

mic

CompleteProcess

Singlereaction

Processcharacteristic

� Interdisciplinary coupling of different physical phenomena.

� Process simulation over different length and time scales.

� Online process simulation with help of high-speed computing.


ECM – Validation of the Simulation Model

Experiment

Inflow

2 mm

z

xOutflowFeed rate

Simulation Cos(φ)

0.27

0

Gas

frac

tion ε /

%

� Cos-Φ method delivers good results in the frontal process gap.

� Multiphysical simulation model couples all relevant physical effects in order to calculate the local dissolution rate of the workpiece material.

� The accumulated gas phase, for instance, leads to lower conductivity of the electrolyte in the outflow area due to the recirculation vortex of fluid flow.


Mold Filling and Solidification in Casting

Case: Structural component for a convertible car

Mold Filling Simulation

Partiell solidification (rapid cooling), shrinkage cavity

Optimization of process parameters (cooling, heating, filling speed)

Quelle: Sigmasoft, Dr Vortrag Puls, WZL 2015

Before Optimization After Optimization


Modeling of the Material Properties - SynchrotronTest Set-Up for the Identification of Phase Transfo rmation

Source: PhD-Thesis Duscha 2014

Detektor

Perkin ElmerArea detector

High Energy Materials ScienceBeamline (HEMS)PETRA III Ring

HEMS Beamline P7

MonochromaticX-ray beam

Induction coil

Workpiece sample

Ceramic punch

F2

F1

2Θ

Tapping of the synchrotron radiation from the storage

ring (PETRA)

Radiographing of the thermal and mechanical loaded workpiece

Detection of the phase fraction and phase

transformation


Outline

�Introduction

�Big Data


�Process Chains and Uncertainty



�Conclusion


Stress and StrainResidual Stress

Surface IntegrityStructure

Grain SizeGeometry

Hard Machining

Turning

Heat Treatment

Hardening

Soft Machining

Extrusion

Forming

Grinding

Manufacturing History



Hard Machining

Turning

Heat Treatment

Hardening

Soft Machining

Extrusion

Forming

Grinding


Manfacturing History

Non linear models(Non) symmetric probability distributionError propagationFunctionalitiy


Outline

�Introduction

�Big Data





�Conclusion


Assistance systemsSupport Men – Machine Interaction

Source: Fraunhofer IOSB

Assistance systemsAssistance systems

Process data

Dataacquisition

Dataacquisition

Visuali-zation

Visuali-zation

Process monitoringProcess monitoring

Recognition ofanomalies

Recognition ofanomalies

Analysis of anomaliesAnalysis of anomaliesUser


Big Data H andling

Server und Datenbank

Prozess-auslegung

Versuchs-feld

Shopfloor 2Shopfloor 1

Ethernet Netzwerk

Quelle: AMC3 ParisTech, afd-pc-service.de, WBA Aachen, forcam systems


Cloud Computing for high performance calculation an d for user-friendly Visualization - Tech-Apps

Cloud computing

Aktivkraft:

Drehmoment:

ConstitutiveModel

High Performance computer

Visualization Tech-Apps

Data


Outline

�Introduction

�Big Data





�Conclusion


FluiddynamicsBasics

EnergyState equation � = �(�, �, , … )

Navier-Stokes (Newton-Fluid) (Navier: 1822; Stokes: 1845)

��

��+ ( ∙ �) = � − � + � ∙ ∆ +

1

3� ∙ �(� ∙ )

Calculation of velocity, pressure, temperature,…

Sir George Stokes

Claude L.M.H. Navier

Bildquelle: Wikipedia, Dr Vortrag Puls, WZL-2015

Material(Newton Fluid)

�� = � ∙��

��+��

��

Momentumconversation law

� = � ∙ ��

Continuity Equ

��

��+ � � ∙ = 0


How to solve ?

� Finite Elemente (FEM)

� Finite Volumen Methode (FVM)

� Meshfree Methods– Smoothed Particle Hydrodynamics– Finite Pointset Method

�WHAT IS LEFT?

�Validation

Drop

Gravity

Bildquelle: Wikipedia, Dr Vortrag Puls, WZL-2015


� Validation is essential

� Engineering science is based on experiments

� Test beds

� High resolution measurement equipment

� Proof of system under realistic conditions (in Industry)


VelocityDatenerfassung beim Crashtest


Velocity – High performance Computer ModellingModell Validation and test rigs

9 x 16 Kraftmessmodule

0.00 0.02 0.04 0.08 0.10 0.120.06Zeit t / s

Kra

ft F

/ kN

1000

800

600

400

200

0

-200

Fahrzeugmasse m = 1800 kg

Quelle: Kistler


CWD – Center for Wind Power DrivesHightech.NRW – „ On-Shore Wind Energy Test Equipment“

Test Bed

� Direct Drive Permanent Magnet

� Power 4000 kW

� Frequenz2 Hz

� Nom. Drehzahl 14 min-1

� Max. Drehzahl 30 min-1

� Torque 2,73 MNm

Power Train

� Winergy Hybriddrive PZFG 2530


Pilot Plants

�Pilot Plants to bridge the gap to the market place

�Pilot Plants to manufacture prototypes to be tested in the field


Project highlight » StemCellFactory «Objectives

� Automated cell isolation of mesenchymal stem cells from bone marrow and fibroblasts from skin

� Automation of a manually established process for the generation of induced pluripotent stem cells iPS

� Automated differentiation of iPS into neuronal stem cells (and cardiac cells)

Methodology

� Re-engineering and optimization of each laboratory process to enable complete process automation

� Choice and integration of all necessary commercially available functional modules

� Design and development of specific functional components and handling solutions

� Development and integration of metrology for inline monitoring of all cell culture processes

Envisaged outcome

� Development and prototype production line of an fully automated, modular demonstrator for the reproducible production of iPS derived cell products

iPS cell clone on feeder cells

CAD model of the StemCellFactory


Adaptive Process Chains

AdaM - Adaptive Production for Efficiency in Energy and MobilityOverall Objective

IntegralStrustures

AdvancedMaterials

Com

plex

ity

Methodic

Transferability

Adaptabilty

Fle

xibi

lity

Data Consistancy

Data AvailabilityCon

tinuo

usD

ata

Flo

w

Ada

ptiv

e P

rodu

ctio

n…

… fo

rre

ssou

rce-

effic

ient

Turb

omac

hine

s

Multi-BliR

Blade cluster

Single blade

Impeller

FinishSchlichtenDrehen FinishSchlichtenDrehen

Process Step 1

Finish Machining

Consistancy

Design Blankpart

Alternative 1

Alternative 2

Alternative 3

Process-Step 2

Process-Step 3

Process-Step 4

Life Cycle Oriented Evaluationof Ressource-Efficiency


Aachen Centrum for Turbo-Machinery

Bilateral Pre-

Compatative

Research Projects

Consulting and Service

Prototyps and Pilotproduction

Manufacturing and Repair

Post-Education

Annual Conference, Seminars, Certificate Courses, Recruiting,…..

AZT


RWTH Campus MelatenFoto Peter Winandy

Research in Aachenwith particular focus on turbomachinery*

Fraunhofer Institute for Laser Technology ILT

Fraunhofer Institute for Production Technology IPT

Laboratory for Machine Tools and Production Engineering

Institute of Jet Propulsion and Turbomachinery IST

Access. e.V.

Surface Engineering Institute

*) Extract to be continued


"If you always do what you've always done, you'll always get what you've always got.“

Henry Ford


"If you always do what you've always done, you'll always get what you've always got.“

Henry Ford

“In life there is something worse than having no success: ‘to have done nothing.“

Franklin D. Roosevelt


Summer School –Student Exchange Program between Germany and Japan


People do matter!

ConclusionThe Keio – Aachen Summer School

Thank you for your attention!

ご清聴ありがとうございました

industrie 4.0 and smart service - 東京工業大学 · bsp.: motor ‐talk.de is the biggest...

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