Industrie 4.0 Challenges and Opportunities for Business ... · Innovation through adoption ... Cloud computing Data mining, safety, security ERP- ... Rudimental load display No information

© WZL/Fraunhofer IPT

Industrie 4.0Challenges and Opportunities for

Business, Science and Society in Germany and Japan

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

Joint Symposium for International Industry-Academia Collaboration

Tokyo March 30, 2015

Seite 2© WZL/Fraunhofer IPT

Outline

� Digitalizationデジタル化

� Big Dataビッグデータ

� Sensorsセンサー

� Case studiesケーススタディ

� Assistance systemsアシスタンスシステム

� New business models新しいビジネスモデル

� Conclusion結論


Global megatrends influence the modern worldWorldwide Initiatives similar to Industrie 4.0


Global megatrends influence the modern worldThe Digitalization of manufacturing is a global tren d

Bringing digital innovation to the physical world Pragmatic adoption of potentials

and long-term strategy

Innovation through adoption

Bringing engineering excellence to the digital world

Radical Innovation

Ability to scale

Speed

Engineering Excellence

Source: P. Kabasci, Fraunhofer IPT, Werkzeugmaschinenlabor, BMBF - INBENZAP


Global megatrends influence the modern worldThe Aachen Approach to Industrie 4.0

cyber

physical

softwarehardware

Single Source of Truth IT-GlobalisationPLM/Engineering-

Systems� Big Data� Cloud computing� Data mining, safety, security

ERP-Systems

� Automation� Sensors� Intuitivity, reliability� Robustness

Shop Floor

CollaborationProductivity- Human/Human- Human/Machine- Machine/Machine

Localdatastorage

Cooperation

� CognitiveSystem

� BusinessCommunities

� SocialCommunities

� ServiceCommunities


Outline









Big Data is a critical aspect of Industrie 4.0Social networks creates data “treasure” that waits to be exploited

Zettabyte

Exabyte

Petabyte

Datavolume

TerabyteGigabyte

Megabyte1438 1878 1926 1969

1981

1991

1998

2007

2010

Internet

Year

„The world‘s information isdoubling every two years.“2

2004

The Internet has changed the private domain - Industry looks for potentials

What happens in an Internet minute? 1

~ 6mMessages

~ 4,1mSearches

~ 350.000Tweets

~ 200.000 Downloads

~ 140.000 $In sales

~ 100 hVideoupload

Source : 1INTEL „What Happens In An Internet Minute“ (2013); 2Gantz „The Digital Universe” (2013), BITKOM „Big Data im Praxiseinsatz” (2012)


Big Data is a critical aspect of Industrie 4.0Data Handling - Google - Big data

Synchronization by meansof atomic clock …

… and every 30 secondsvia GPS.

Many Googles data centersacross the world…

Source : Promotionsvortrag Dr.-Ing. Cathrin Wesch-Potente, www.wired.com/2012/11/google-spanner-time/all/, www.golem.de/news/, www.images.zeit.de, www.apfelpage.de, www.extremetech.com


Big Data is a critical aspect of Industrie 4.0Production industry is at a beginners level in usin g Big Data

Production Service

Relevance of digitalization in SMEs for different branches1:

33%

17%

50%

Less imortant

Important

Very important

Producing industry: High relevance of digitalizationhas been recognized, but it is hardly utilized!

Source: 1Deloitte „Digitalisierung im Mittelstand“ (2013) 2Capgemini-Studie „Digitalizing Manufacturing: Ready, Set, Go!“ (2013)

22%

45%

33%

Dig

ital I

nten

sity

Intensity of transformation management 2

Fashionistas

Beginners Conservatives

Digirati

Productionindustry

Insurancecompanies

Banking

Telecommunication

Retail industryTravel industry

Pharmaindustry

Consumergoods

EDVHigh technology

Utilitycompanies


Outline









Global megatrends influence the modern worldThe Aachen Approach to Industrie 4.0

cyber

physical

softwarehardware

Single Source of Truth IT-GlobalisationPLM/Engineering-

Systems� Big Data� Cloud computing� Data mining, safety, security

ERP-Systems

� Automation� Sensors� Intuitivity, reliability� Robustness

Shop Floor

CollaborationProductivity- Human/Human- Human/Machine- Machine/Machine

Localdatastorage

Cooperation

� CognitiveSystem

� BusinessCommunities

� SocialCommunities

� ServiceCommunities


Sources of InformationData Sources in Manufacturing – Cloud computing

Dynamics of Machine, Workpiece, Clamping

Product, ProcessMachine

Sensors, Drives, Positioning, NC-Controller

Workflow, Carrier (RFID), ERP, PDM, MES,Process Models

Big Data


Cloud computing

Big Data Handling and ProcessingComputing

� Position data� Vibrations� Forces

� Torque� Lubricants� AE

� Geometry� Temperature

Data AnalysesUncertainties

StatisticTrend Analyses

Complex Process Modeling

Advanced Data Mining

New Business Models

Model ReductionFast Algorithms

HeuristicsMachine Tool Work piece


Machine-integrated sensors Several sources of information

Augmented Machine

Tool

Product

Machine

Process

Tool Deflection

Tool Wear

Roughness

Tool type, no. of cutting edges, …

Machine type, …

Material, important characteristics, features, …

Process strategy, machine operator

Detected at specifically defined characteristics

Measured at defined points in time

Surface roughness measured at defined characteristics/features

Funded by Manonet Project Source

Source: Kern Machine Tool Company


CombinationsDifferent information sources for knowledge acquisi tion

� Obtaining data of better quality

� Reliability and accuracy improvement

� 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


Outline









Case study 1: Blisk production in geared turbo fansGeared Turbo Fan PW1000G

Source: Pratt & Whitney

High degree of product varietySteady increase of production system parameters


Case study 1: Blisk production in geared turbo fansModeling the whole process chain

5-axis HSC

milling

5-axis HSC

millingGrindingGrinding PolishingPolishing……

3-Axis milling3-Axis milling

Horizontal process chain

Vertical process chain

Post-process� Maschine- specific

NC-Code

CAM� Cutting strategy� NC-Programming� Process parameter Simulation

� Optimization of NC-Programme

� Tool optimization� Collision control

Machining� Cutting� Quality management� Process monitoring


Holistic ModelingMatching of Virtual Production and Real Production - Clou d

5-axis HSC

milling

5-axis HSC

millingGrindingGrinding PolishingPolishing……

3-Axis milling3-Axis milling

Virtual ProductionCAM

SimulationPost Processing

Functionality Prediction

Real ProductionDisturbancesFluctuations

Order ChangeOptimization


Vertical Chain: Make use of process models on different s calesFrom rule based heuristics to complex simulation

MolecularDynamics (MD)

Kinematics

Fundamental

Finite Element Analysis (FEA)

Regression

Artificial NeuralNets

Rule Based

mac

rosc

opic

�m

icro

scop

ic

heu-

ristic

phys

ical

empi

rical

x

x xxx

xxx xxxx

mx+bx-cx = CU0sin(ωt)

x, x, x

Sources: CIRP Keynote Paper 2006, Brinksmeier et al.)

Am

ount

ofD

ata

Big Data All Models are WRONG!

But some are useful!

George E.P. Boxhttp://1.1.1.1/bmi/upload.wikimedia.org/wikipedia/commons/thumb/a/a2/GeorgeEPBox.jpg/220px-GeorgeEPBox.jpg


Case study 2: Vertical Chain - Gear MakingGeared turbo fan PW1000G

Source: Pratt & Whitney


Case study 2: Complex process modelingGrinding as core process in gear production

Production of different gears…

…requires complex grindingprocesses…

…and thus high know-ledge about the tool

1234

5S8

S7

S6

S5S4 S3 S2

S1

vcy


Case study 2: Modeling of Grinding Tool SytemsBig Data and small data

Grain Size Distribution

%

%

%%

Specification

Grain Volume

Bond Volume

Grain Shape Distribution

Pore Volume

Grain Shape Grain Distribution Bonding Material

RealTopography

Structure

Real GrindingProcess

Modeled Topography

Comparison

Transfer

Dressing Parameter

KinematicGrinding Model

Rea

lity

Mod

el


Case study 2: Prediction of FunctionalityProcess Monitoring

Image sources: Badische Zeitung, WZL, Universität Bochum, Lotus,

Precision

Budapest, Oct. 2009

Critical Collapse

Herzberg Sept. 2011

Hockenheim, July 2014

Pitting

Root break

Consequential Damages

Frettingg

Failure Initiation

Grinding burn

SEM picture of crack

Micro pitting


Case study 3: Modeling of transient Material Dissol ution in EDM Compressor Blade

Inflow

2 mm

z

xOutflowFeed rate

TOOL (Cathode)

WORKPIECE (Anode)


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.


Case study 3: Inverse Cathode Design ECM – Validation of 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.


Learn from the past – share knowledgeOpen space data base of Federal Aviation Administra tion

FAA Lessons Learned Database

Source: http://lessonslearned.faa.gov/ll_main.cfm?TabID=1&LLID=6

� Covers all worldwide aviation accidents since 1953

� Gives data about the causes of failure

� Free access via the Internet

Description ofthe accident

Meta-data

Perspectives


Combining sensors and modelsSensor based models

Worldwide database

22 billion measurement parameters

Optimization of manufacturing and

assembly processesPrediction of the module‘s failure

probability in serviceSource: Fa. BOSCH

Measurement data fromvarious sensors

Worldwide networkedfactories

Data acquisition of all modules

Field data, operation


Outline









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


Cutting forcefrom signal

Cutting forcefrom model

Process related display of active forceand torque

App-ConceptInitial Situation

Rudimental load display

� No information about process(large face mill/small shank mill))

� Process related Interpretation of toolload possible

� Builds knowledge for machine operator

Aktivkraft:

Drehmoment:Model comparison

HeuristicsTech app – Smart support based on rules and experience


Run complex simulation in the cloud –Decision is taking by men on the shop floor

Dynamic optimization

ToolingProcess

Image Sources: Index-Werke, WZL, AWK 2014 - Brecher

Analyses and computation


Outline









Power by the hourGathering of field data to enhance product value

Source : www.rolls-royce.co.uk, www.obs.co.uk

> 300 pressure + temperature sensors Engine monitoring

creates Big Data

Signals for determination of maintenance

Worldwide MRO CentersPower by the hour


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

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







� Conclusion – Roadblocks and Perspective結論


StandardsEstablishment of consistent standards is a crucial point for SME‘s

Business

Functional

Information

Communication

Integration

Asset

Layers

� Classifies all entities of Industrie 4.0:

– Layers

– Value stream

– Hierachy levels

� Allows stepwise migration from today’s world into Industrie 4.0

� Proved in several use cases

Source: Http://www.zvei.org/Presse/Presseinformationen/Seiten/Wichtige-Etappenziele-bei-Industrie-40-erreicht.aspx

RAMI: Reference Architecture Model for Industrie 4.0


�Machine to machine integration using a read-only model

�Peer to Peer (P2P) communication

�Secure – only receive data from trusted sources

�Supports n-to-n communicationSource: http://www.elektroniknet.de/automation/m2m/artikel/112702/AMT - The Association For Manufacturing Technology

StandardsMT Connect Standard – Bridge between MES and ERP


StandardsDMG-Mori Machine Network – Monitor performance in real time

Source: http://www.dmgmori-usa.com/ Dr. Linke, University of Davis

• Network of all machines and monitor them in real time• Improve machine utilization• Alarms, work counts, overrides are monitored• Comes with other standards, run time, operational history, • pareto analyses


Roadblock - Big Data - Infrastructure Development in data transfer allows for scientific collaboration

Source: Co. Corning, Co. Intel, globasure.net

New fiber and connector technologyenables an ultra fast data transmission.

Enabling cloud computing, big data andnew global network architectures.

Up to 1.6 terabits per second


RoadblockSafety and Security of CPS need to be adressed

Data security

IPR

Cyberattacks

Safety Security

Bildquelle: Kuka.de, Elektroniknet.de, ingenieur.de, trialog.de

Blackout

Men MachineInteraction


Source: VDE-Trendreport 2013, Befragung Unternehmen und Hochschulen, AWK 2013, Prof. Matthias Jarke

Roadblock – Safety and SecurityCyber Physical Network CPS, VDE-Trend Report 2013

IT Safety and Security

No (less) Standards

Qualification of People

High Performance Infrastructure

Capital Investment In TotalCompaniesUniversities


RoadblocksSafety and Security need to be adressed

Data security

IPR

Cyberattacks

Safety Security

Bildquelle:Fraunhofer Group IuK Technologies, Chairman: Prof Matthias Jarke , uka.de, Elektroniknet.de, ingenieur.de, trialog.de


ConclusionCooperation creates win-win situations

Japan and Germany share manifold similarities

� GDP and industry

� High technology

� Export-countries

� Population and social structure

Population [million]126.43 80.72

GDP [trillion $]4.788 3.820

Secondary sector27.5 % 24.4 %

Mean age (World rank)44.6 (2) 43.7 (4)

Population below 2019.1 % 19.2 %

4m 2m 2m 4m0Population 2005

Men Women

6m 3m 3m 6m0Population 2005

Men Women


Collaboration – CPS in Manufacturing EngineeringWhat can we do togehter?

� Big Data Analytics and Complex Physical Modelling of Processes (any kind)

� NSF (Japan and Germany) to set up a joint fund

� Assistance Systems (Sensors, Modelling)

� Joint teams from university and industry

� Exchange People

� Ask University Presidents to support


ConclusionCollaboration productivity greatly enhances profita bility

„Who works alone, adds on.

Who works together with others, multiplies.”

Old Arabic saying

In a nutshell: Industrie 4.0 for collaboration productivity


People do matter! - Many good reasons to cooperate!

ConclusionThe Keio – Aachen Summer School


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

Documents

Industrie 4.0 Challenges and Opportunities for Business ... · Innovation through adoption ... Cloud computing Data mining, safety, security ERP- ... Rudimental load display No information