TA CR Day - Industrie 40 (Ralf Wehrspohn, Fraunhofer Institute)

© Fraunhofer

Fraunhofer Industry 4.0 strategy

Ralf Wehrspohn

Fraunhofer Institute

for Microstructure of Materials and Systems IMWS

20.10.2016,

Smart Living Conference

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1 Fraunhofer-Gesellschaft

Challenges in Industrie 4.0 technologies

Health and Environment

Production and Supply of Services

Mobility and Transport

Energy and Resources

Security and Protection

e.g. autonomous vehicles, decentralized multi-agent logistics … Communication and Knowledge

e.g. data security and safety, data rate and latency, deep learning …

e.g. Cyber Physical Systems, predictive maintenance, customized and adaptive production ...

e.g. cyber security, trusted data exchange, resilient systems …

e.g. closed-loop production, energy self-sufficiency, intelligent grids

e.g. Human-Machine-Interaction/Cooperation

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Mechanical loom Late 18th century

Assembly line at Ford Early 20th century

Smart Factory Today

CNC control unit Early 1970s

1st industrial revolution: • Mechanical production facilities • Water and steam power

2nd industrial revolution: • Specialized mass production • Electric power

3rd industrial revolution: • Automatisation of the production • Elektronics and IT

4th industrial revolution: • Cyber-Physical-Systems

Participation Flexible processes Consumption oriented

Cooperation Adaptive Real-time processes Order related

Authoritarian leadership Rigid processes Forecast oriented

2 Industrial Transformation – Frame Conditions

The 4th industrial revolution

Darstellung basierend auf DFKI Bildquelle: v.l.n.r.:

Deutsches-Museum; Hulton Archive/Getty Images; http://autoworks.com.ua; http://www.automationspraxis.de

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Drivers of the 4th Industrial (R)evolution

Industry

New products

New processes

New markets

Socio-economic framework

Innovation players

Demographic change

Changing consumption

Market

competition

expectations

changes

R&D and technology

Acceleration through ICT

»Intelligent« technology

Shorter innovation cycles

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National platform Industrie 4.0

INDUSTRIAL DATA SPACE

Industry: Bernd Leukert (SAP), Reinhard Clemens (Telekom), Eberhard Veit (Festo), Siegfried Russwurm (SIEMENS) Research: Reimund Neugebauer

Management BM Gabriel, BM‘in Wanka

Technical-practical competency, decision-makers

Political governance, society, opinion leaders

Market activities

Strategy Group (Politics, associations, unions, research) Fraunhofer: Prof. Bauernhansl, IPA

Steering Committee (Business)

Working Groups Scientific Advisory Board

Industrial consortia and initiatives

International standardization

Committee as service provider

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Software Hardware

Cyber

Physical Cooperation Automation

IT-Globalisation Single Source of Truth

Human / Human

Human / Machine

Machine / Machine

Collaboration-productivity

Data storage Data distribution Data analysis

Adaptive Intuitive Robustness

ERP Systems PLM Systems Engineering Systems

Business Communities Social Communities

ERP

Local data storage

Cognitive system


Industrie 4.0 – IT merges with manufacturing technology

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2 Industrial Transformation – Enterprise Level Dimensions of Industrie 4.0: Fraunhofer »Layer Model«

Enterprise Transformation

Business models

Management

Human resources

Neugebauer, Hippmann, Leis, Landherr, Industrie 4.0 – From the perspective of applied research, CMS CIRP Conference on Manufacturing Systems, Stuttgart, May 25th 2016

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Business models

Management

Human resources

Information and Communication enabling Technologies

Standardization

Data rates and low latency communication

Data security

etc.


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Business models

Management

Human resources

Information and Communication enabling Technologies

Standardization

Data rates and low latency communication

Data and cyber security

etc.

Data-driven Production Technologies for Industrie 4.0

Cyber-Physical-Systems

Machine Learning in Production Processes

Autonomous Systems

etc.


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2 Industrial Transformations – Potentials and challenges German economic potential of Industrie 4.0

Forecast until 2025:

Up to 430,000 new jobs, but simultaneous elimination of 490,000 low-skilled jobs*

GDP growth of about 30 billion EURO **

Total investment of about 250 billion EURO **

Source: *Studie: IAB, BIBB, GWS (November 2015), **BCG-Studie: Industry 4.0 (April 2015) from Prof. Neugebauer SFU 2015

Industrie 4.0

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2 Industrial Transformations – Potentials and challenges

Size in 2025, $ trillion1

Nine settings where added value is expected

Factories – eg., operations management, predictive maintenance 1.2 - 3.7

Cities – eg., public safety and health, traffic control 0.9 - 1.7

Human – eg., monitoring and managing illness, improving wellness 0.2 - 1.6

Retail – eg., self-checkout, smart customer-relationship 0.4 - 1.2

Logistics – eg., logistics routing, autonomous vehicles, navigation 0.6 - 0.9

Work sites – eg., operations management, equipment maintenance 0.2 - 0.9

Vehicles – eg., condition-based maintenance, reduced insurance 0.2 - 0.7

Homes – eg., energy management, safety and security 0.2 - 0.3

Offices – eg., augmented reality for training 0.1 - 0.2

Total $ 4 trillion - $ 11 trillion

Global economic potential of the Internet of Things

Low estimate

High estimate

1Adjusted to 2015 dollars, for sized applications only; includes consumer surplus. Numbers do not sum to total, because of rounding Source: McKinsey Global Institute analysis, June 2015

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Protecting know-how and competitive advantage

Industrial technologies

Mechanical Engineering

Internet of Things

Machine Learning

Traditional strengths: Hardware, industrial machines microelectronics, embedded systems, sensors, automotive

Traditional strengths: Software, networks, server, clouds, Big Data, Artificial Intelligence, IT-Services

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Player Situation Goals Means

Industrie 4.0 Germany Growing competition

Leadership in Cyber-Physical-Systems

Integrating ICT into manufacturing

Industrial Internet

USA, UK Service-centred economy

Re-industrialization Adding manufacturing to ICT

Full Automation

East Asia Labour shortage, rising labour costs

Cheaper, faster, less labour

Using robots for manufacturing


Directions for the future of manufacturing

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»The spread of computers and the Internet will put jobs

in two categories. People who tell computers what to do,

and people who are told by computers what to do«

Marc Andreessen (*1971 USA) Founder of Netscape Communications and Developer of Mosaic, one of the first successful international web browser


Challenges

Machines will perform tasks with repetitive character and low complexity

Hard- and software will perform planning tasks autonomously

Knowledge workers with medium qualification will be supervised and monitored by computers »Human Automation«

New types of jobs emerge

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3 Requirements for data-driven production

Automotive companies

Electronics and ICT

Services Logistics Mechanical engineering

Pharmaceuticals & Medicine

Service- and product innovation

»Smart Data Services« (alerting, monitoring, data quality etc.)

»Basic Data Services« (information fusion, mapping, aggregation etc.)

Internet-of-Things ∙ Device proxies ∙ Network protocols

Embedded Systems ∙ Sensors ∙ Actuators

INDUSTRIAL DATA SPACE

Standardization Council Industrie 4.0

Standards

Data security

Machine Learning

New markets

Digital Economy – Smart Services

Ta

ctil

e In

tern

et

– lo

w l

ate

ncy

Broadband expansion - Data Rate ENABLER

INDUSTRIAL DATA SPACE© – Digital and Data Sovereignty

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3 Requirements for data-driven production – Standardization

Reference-Architecture-Model Industrie 4.0 (RAMI 4.0)

Three-tier system

Joint development by: Bitkom, VDMA, ZVEI, Plattform Industrie 4.0

Source: © ZVEI

Dig

ital

Rep

rod

uct

ion

Standardization goals I4.0:

Identification (location of participants)

Semantics (communication)

Quality of service (low latency, reliability)

Requirement: Standardization

compatibility and interoperability

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3 Requirements for data-driven production – Industrial Data Space

Secure data exchange and data sovereignty: INDUSTRIAL DATA SPACE©

Retail 4.0 Banking 4.0

Insurance 4.0

… Industrie 4.0 Focus:

manufacturing industry

Smart Services

Data transmission Networks

Real-time / low latency

Industrial Data Space Focus: Data

Data

… Optimization:

processes, market

Disruptive Innovation

Evolution

New Products New Markets

Industrial Data Space

Upload / Download / Search

Internet

Apps Vocabulary

Industrial Data Space Broker

Clearing

Registry Index

Industrial Data Space App Store

Internal IDS Connector

Company A Internal IDS Connector

Company B

External IDS Connector

External IDS Connector

Upload

Third Party Cloud Provider

Download

Upload / Download

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3 Requirements for data-driven production – Industrial Data Space

Industrial Data Space – Digital sovereignty for Industrie 4.0

Highlights January 2016: Registered

Association founded Round-table on EU-level CeBIT and Hannover Messe

Fraunhofer-Consortium 12 Institutes AISEC, FIT, FKIE, FOKUS, IAIS,

IAO, IESE, IML, IOSB, IPA, ISST, SIT

Members of the Industrial Dataspace Association:

Key data of the BMBF-Project Start: 1.10.2015

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3 Requirements for data-driven production – Low latency communication

Requirement: Low latency for the tactile Internet

Source: VDE-Positionspapier Taktiles Internet

Human reaction times

… as communication network with minimal signal delay

Realized through e.g.: 5G, WiFi and wired communication networks

Enabling new applications in

Industry (as part of the industrial Internet: e.g. robotics)

Mobility (as part of the transport network. e.g. collaborative trial)

Assistance and health (Augmented Reality, Interactive learning)

1000ms 100ms 10ms 1ms

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System architecture for the mobile communication of the future (2019)

via »Rainforest-Approach«

3 Requirements for data-driven production – Low latency communication

Tactile Internet network architecture

Requirements:

1000 x data throughput

100 x no. of devices

10 x battery life

1 ms latency

Car2Car & Car2X Communication

Industrie 4.0

Mobile high-speed Internet

Use cases 5G:

Decimeter waves FR: 300 MHz … 3 GHz Radio communication, air traffic control, mobile phone

Centimeter waves FR: 3 … 30 GHz Microwave link, satellite broadcasting, RFD

Millimeter waves FR: 30 … 300 GHz »See-through walls«, ABS-cruise control, security scanner

HHI, IIS, FOKUS, AISEC

Providing coverage

Hot spot performance

Device to Device

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3 Requirements for data-driven production – Machine Learning

Machine Learning for optimized production processes

Training

Big Data Architectures

Data Resources

Deep Learning

Complex Knowledge

Reasoning

Industrie 4.0

Healthcare

Logistics

Smart Enterprise

BIG DATA SMART DATA KNOWLEDGE ADDED VALUE

Raw Data Information Decision Productivity

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Flexibility, versatility

Interactivity, communication skills

Iterativity, memory

Contextual analysis, adaptability

3 Requirements for data-driven production – Machine Learning

»Machine Learning« leading to »Cognitive Machines«

Machine Learning (ML): »procedures of Artificial Intelligence that enable machines to learn from (data)examples in order to optimize their (decision) processes without being explicitly programmed.«

ML as enabler for »Cognitive Machines«

Progress through »Moore’s Law«:

processing speed

data storage

»clouds«

»big data

fast internet

miniaturization

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Competitive Service Portfolios are Increasingly Hybrid, as the Example adidas Shows:

time

hybridity

Physical product

(running shoe)

»classic service«

(training monitor)

digitalized service

(Social Network-Integration)

Images: otto.de (2015), techglam.com (2015), soccerreviews.com (2015).

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Data as a Strategic Resource

time

value contribution

Data as process results

Data as process enablers

Data as product enablers

Data as products

Sensors

NC-Machines

CAD/CAM

Big Data

New Business Models

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Digitization as driver and enabler of innovative business models

Automotive

traffic management 2.0

Dynamic routing

»Connected Drive Services«

service

innovation

Production

Intelligent manufacturing concepts for small series

Self-controlled manufacturing

organizational

innovation

Pharma

»Real-Life Evidence«

More effective and efficient therapy

Personalized medicine

product

innovation

Commerce

Autonomous transparency along the supply chain

Consumer-centric supply chain

process

innovation

Ass

et

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Economy 4.0 - New Business Models Based on Different Data Sources

Automotive

Automotive suppliers

Traffic control center

Cities and municipalities

Production

Automobile manufacturers

Suppliers

Logistics provider

Pharma

Pharmaceutical companies

Pharma. research

Healthcare Provider

Physicians

Commerce

Retail

Consumer industry

Logistics provider

Transport vehicle pools

Diagnostic data, pathologies

Therapy information

Location, Destination

Vehicle data

Traffic data

Transport data

Environmental data

Product-, components data

Planning data

Transport status

Data

Part

ies

invo

lved

New Business Models

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4 Fraunhofer R&D for Industrie 4.0 – Smart logistics

Goal:

Paper-free documentation of the travel paths of

an air-freight container

Autonomous air-freight container iCON:

High-resolution ePaper display for cargo data

Monitoring and reporting of environmental

parameters

Positioning by GPS and GSM roaming data

Worldwide data exchange : 4G LTE network /

UMTS / GSM

Identification by QR-Code on the display

Solar energy harvesting:

LiPo energy buffer for 3-6 months darkness

Hardware for the Industrial Data Space – the intelligent container iCon

Hardware with TraQ from:

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4 Fraunhofer R&D for Industrie 4.0 – Smart logistics

Smart, networked and autonomous swarm-logistics

BinGo: Drone-based Transport System

Decentralized multi-agent system with advanced »ant colony« algorithm

Energy-efficient and safe: Mainly rolling and only flying when necessary

Battery charging while descending on rail-equipped spirals

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4 Fraunhofer R&D for Industrie 4.0 – Human integration

Challenges: Utilization of Industrie 4.0 applications for competence development and real-life learning environments

Requirements

Process understanding, integration and real-time synchronization of processes throughout the product lifecycle

Transversal skills development and training (IT, electronics, mechanics etc.)

Generic competences about organization, communication and cooperation

High flexibility and decision-making capability

Developing Industrie 4.0 competencies

Learning factories 4.0 Project work, simulations Participation ramp-up

Solutions for competence development: Fraunhofer »FUTURE WORK LAB«

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Processes past 100% anonymous boards

Supplier certification o.k. Cutting

Coil storage

Quality

No knowledge about the causes for faulty parts

4 Fraunhofer R&D for Industrie 4.0 – Processes and connectivity Challenge: Finding the cause for faulty products

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Processes future

Verifiability with INDUSTRIAL DATA SPACE©

Cutting

Coil storage

Quality

i

Optimized manufacturing parameters of cyber-physical tool for current board

Integrated material tester and board marking

Supplier certificate for every meter

i

i

i

4 Fraunhofer R&D for Industrie 4.0 – Processes and connectivity

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4 Fraunhofer R&D for Industrie 4.0 – Machine Learning

Machine Learning for milling and cutting methods

Efficient resource usage

cooling

lubricants

compressed air, …

Optimized tool paths

quality / finish

energy efficiency

tool wear, …

Industry 4.0 and Big Data to support future intelligent manufacturing

more relevant data (I4.0) and correlations between them (Big Data)

rich data base for application of machine learning

Cloud Computing and Deep Learning for manufacturing data

Machine design

data-based modelling

structure / material

precision / rigidity, …

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4 Fraunhofer R&D for Industrie 4.0

Electric car without driver

Challenges: Secure autonomous driving with comfort and efficiency

Fraunhofer solution: electric car without driver

E-car parks autonomously

E-car finds charging stations without the driver

E-car navigates safely with help of modern sensors and IT

© Fraunhofer IPA

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Intelligent IT-Assistence for everyday life

Fotos: Bernd Müller, © Fraunhofer IAO

Iris-Scan

replaces access card (ATM, hotel, appartment)

ergonomic adjustment (hotel, workplace, …)

Interactive, collaborative Virtual Reality Conference

Smart Energy Control

Synchronization of private media library with external media systems

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Fraunhofer: Hacker protection for Smart Homes

Challenges: A growing number of household functions can be controlled via internet and are therefore exposed to cyber attacks and hacking

Fraunhofer solution: Software protection between internet and IT of the building

»Firewall« blocks harming communication flow

Useable for all kinds of building IT technologies

No hardware exchange necessary

Sou

rce

: Fr

au

nh

ofe

r FK

IE

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Human-Machine-Collaboration

Care-O-Bot4 © Fraunhofer IPA

Safety

Highly developed sensors/actors

Intuitive communication

Situational awareness

Adaptability

Learning ability

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5 Outlook

Challenges and Chances for Implementation of Economy and Industry 4.0

Innovative Business Models

Security Trusted

Networks

Latency Data Rates

Tactile Internet, Intelligent Grids

Compatibility Standards Branches

Pro

cess

Ch

ain

s

Economy 4.0 In

du

stry

4.0

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5 Conclusion

Essentials for implementing Industry 4.0

Latency and Data Rates

Tactile Internet, Intelligent Grids

Security Trusted Networks

Compatibility Standards

Machine Learning

Process Optimization, Human collaboration

ICT skills Human Resources

TA CR Day - Industrie 40 (Ralf Wehrspohn, Fraunhofer Institute)

Technology

Transcript of TA CR Day - Industrie 40 (Ralf Wehrspohn, Fraunhofer Institute)