trends that shape the future of automation engineering · prof. dr.-ing. michael weyrich trends...

Prof. Dr.-Ing. Michael Weyrich

Trends that Shape the Future of Automation

Engineering

On Cyber-physical Systems, Smart Components

and other Innovations

Institute of Industrial Automation Technology and Software Engineering (IAS)


03. April 2014

© 2014 IAS, Stuttgart University


Institute of Industrial Automation and Software Engineering

(IAS); Faculty of Computer Science, Electrical Engineering and

Information Technology of Stuttgart University

© 2014 IAS, Stuttgart University 2

Research and Teaching of the Institute is focused on software systems for automation engineering and is based on our background in information technology, software and electronics. We are researching towards applications of automated manufacturing, automotive and consumers products.


3

Resume

Education in the area of mechatronics • Studies of Electronic Engineering and Control Engineering at

University of applied science (Saarbrücken), Ruhr University

(Bochum) and University of Westminster (London)

• Doctorate, RWTH (Aachen) in Mechanical Engineering

Daimler • Member of the exchange group research and technology

• Digital Factory Powertrain and Head of Function

CAx Process Chain Production

• Head of Department Engineering Services, Bangalore (India)

Siemens Automation & Drives /Motion Control • Head of Department, direct Report to Head of Business Unit

University Professor • Chair of Manufacturing Automation, University of Siegen

• Institute of industrial Automation Technology and Software

Engineering, University of Stuttgart

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• State-of-the-Art and Vision

• Cyber-physical Systems

• Technologies for the application domains

Automotive and Consumer products

Manufacturing Industries

• Aspects of research, open questions and conclusion

Contents



Which Technologies are promising changes

towards Disruptive Innovation?

The „Gardner Hype Cycle“

methodology provides a view on

how technologies could evolve

over time

(here: personal assessment of

buzz words )

Expecta

tions

Big Data

“Industrie 4.0”

AI

Organic

Computing

CPS

Technological

Trigger Peak of

inflated

Expectations

Disillusionment Plateau of

Productivity



Success story of the Apple-iPhone: on how Apple revolutionized the business model based on technology

Source of picture:

Apple

Technology: new chips,

data centers and

software (e.g. Apps)

Useful and elaborated–

sophisticated functionality

activates the genius of

developers

Redefinition of the

utility value of a phone – meets requirements

which were unavailable to

users before

Business Model:

creation of an

ecosystem for Apps and

downloads such as music,

games and navigation

Prime Example for Disruptive Innovation



Vision: Smart Units / Nodes in the Internet of things and services are automating the world

Source of Picture: Wikimedia Commons


//upload.wikimedia.org/wikipedia/commons/d/d2/Internet_map_1024.jpg








Contents



1991

Mark Weiser

“Cyber-Physical Systems”

XEROX PARC

2006 2010 Common

Definition )*

A cyber-physical system (CPS) is a system of collaborating computational elements controlling physical entities )* Wikipedia En, 23.03.14

The Computer For the 21st Century

NSF Workshop

acatech

Vocabulary and Concepts

Sourc

e o

f pic

ture

: In

tern

et


Weiser: “Next comes

ubiquitous computing …

when technology recedes

into the background of our

lives”; “… it won't be done

until everything is on the

Web …”

Cyber-Physical Systems

Systems of Systems

Embedded Systems

“Ubiquitous Computing”


Sourc

e:

Based o

n :

Gausem

eie

r, U

niv

. P

aderb

orn

Actuators Sensors

Information

processing

Control

Human

Environment Object / Unit

Human-

machine

interface

System Systems

Communication

Infrastructure

Smart, networked Systems as a game-changer: New ways of cooperation among distributed and intelligent units as well as with human

Cyber-physical Systems


Cooperative

Systems


Sourc

e:

Based o

n :

Gausem

eie

r, U

niv

. P

aderb

orn

Actuators Sensors

Information

processing

Control

Human

Environment Object / Unit

Human-

machine

interface

System Systems

Communication

Infrastructure

Cyber-physical Systems

Learning and adapting

during runtime simplifies

the configuration

Improved human-machine

communication for

interaction

Architectures and

methods for quick and

easy integration

Evaluation and test of

Smart Units


Cooperative

Systems

Smart, networked Systems as a game-changer: New ways of cooperation among distributed and intelligent units as well as with human








Contents



Research in Automation Technology revolves

around the application domains


Urban Life

Automotive



• The number of ECU (Electronic control units) and bus systems is increasing

• Complexity of Electric / Electronics and software increases due to driver assistant systems and connected cars

• Process improvement and quality

management in development (AUTOSAR Standard)

• Model-based development and simulation (SiL, HiL)

Networking Technology of the Car changes the

process of Development

Sourc

e o

f pic

ture

s: D

aim

ler

AG

Example Daimler (E class)

1995

(W219) 2002

(W211)

2009

(W221)

2013

(W212

MOPF)

Number

92 control

units (ECU)

14 bus systems 15

2



App based diagnosis of

household appliances

Urban Life is being automated Award-winning research for automated consumer products

Multi-modal HMI of a ticket

vending machine

RFID automates a drug cabinet

Coffee from the internet



“The New Digital Age – Transforming Nations,

Businesses and our Lives“ )* Vision of Google: omnipresence of Cyberspace and physical world

in the sense of a hybrid reality.

)* see book of same name: Eric Schmidt (Goggle Executive Officer) and Jarred Cohen (Head Think

tank Google Ideas);

Paradigm change:

Merging information from

the internet, mobile

communication und the

physical world

Google

Project

Glasses

Navigation Location- dependent

information

Human machine

interface

Sourc

e o

f pic

ture

s: G

oogle

, h

ttp:/

/ww

w.y

outu

be.c

om

/watc

h?v=

JS

nB

06um

5r4


http://www.youtube.com/watch?v=JSnB06um5r4


Sourc

e o

f pic

ture

s: S

warm

Lab,

UC

Berk

ele

y

Swarm operating

system

New operating

system as mediating

layer between apps,

resources and the

cloud?



The level structure in automation (so called automation pyramid) is

dissolving

Decentralized services are self-organizing and any hierarchy becomes

blurred

Real-time Systems remain however for some time on the bottom field level

in the close future

(Partial) Decentralization of the Architecture

Source: VDI, Hypotheses and fields of action: Opportunities form the perspective of automation technology, April 2013









Contents



“Industrie 4.0” Integration, Internet of things and services as well as Standards in the field of Big Data, Security and the business areas)1

)1 Result of a meeting at the VDI Düsseldorf on10. Juli 13: Participants: Prof. ten Hompel, Fraunhofer IML; Klaus Bauer, Trumpf; Dr.

Dagmar Dirzus, VDI Wissensforum; Dr. Ralf Ackermann in SAP Research Center; Prof. Dr. Dieter Wegener, Siemens AG, Dieter

Westerkamp, VDI; Prof. Dr. Michael Weyrich, Universität Stuttgart; Christoph Winterhalter, ABB AG; Dr. Heyjo Jacobi,VDI

Sourc

e:

VD

I



Case Study: “Industrie 4.0” Demonstrator

Cooperation between university institutes of the Automation Community

Application Scenario “My Yogurt“

• Individual Product Configuration: Customers can order various amounts of

yogurt of different flavors via the internet.

The yogurt is produced by different

machinery all around Germany.

• Diagnosis of the distributed

machinery: In the event of failure of similar units, an

inquiry can be launched to obtain

information on how the incident was

resolved at another system. This approach

can also be deployed for preventive

maintenance.

Scourc

e: [C

hristia

n D

iedrich,

OvG

U]

AISTU München

DFKIKaiserlautern

LS Informatik 11RWTH Aachen

IFAT & ifakOvGU Magdeburg

IASUniversität

Stuttgart

IATHSU Hamburg

IAITU Dresden

VerbundeneAnlagen


Connected

machinery

Systems


Case Study: Research Aspects

Technology

• Scheduling, Modelling of

Processes

• Apps for human machine

communication

• Service-oriented Architecture

• Network Management - Web

Based Enterprise Management

• Cloud-Services for validation of

embedded systems

Requirements

• Configuration of products with

interactive resource allocation

• Diagnosis to improve reliability

• Reliable and easy to use by operators

• Flexible and re-configurable in the

sense of interoperability, adaptively

and ad hoc cooperation

Scourc

e: [C

hristia

n D

iedrich,

OvG

U]



Automation Architecture based on Agents

• Agents are autonomous entities

• Agents achieve goals and may learn

• Agents interact with the environment and other agents

AbfüllanlageT14

FräsanlageT13

MischanlageT12

GäranlageT11

CAN/Ethernet

Gateway

Internet

CAN

Agent Agent Agent

Agent

Management

System

Directory

Facilitator

Web Socket

Agent Agent Agent

Agent

Management

System

Directory

Facilitator

Web Socket

Agent Agent Agent

Agent

Management

System

Directory

Facilitator

Web Socket

Agent Agent Agent

Agent

Management

System

Directory

Facilitator

Web Socket


Fermentation Mixing Milling

Bottling


„Intelligent Bin“ requests for a refill

on its own

Source: Fraunhofer IML, Prof. Dr. Michael ten Hompel

iBin is counting the parts using an integrated camera. The

system interacts with the cloud for part logistics

“Smart Units“ are dedicated towards special application thereby fulfilling a particular customer value

Examples for Smart Units

Intelligent Bus Terminal facilitates

signal processing and control

Source: Beckhoff









Contents



Diagnosis

Condition

Monitoring

Re-

configuration

Quality Control

Documentation

Field Level (Real time level): Process Control, Sensors,

Actuators etc.

Coordination layer: Agents with directory of services

and data processing

Services for human machine

Communication (Apps)

Hierarchies are fading and borders become blur

Decentralized services are partially self-organised by agents

Decentralization of Architecture

Assignment of production jobs



Smart engineering through learning and self configuration

Method: Configuration of automation systems through an agent-based Configuration of preconceived Components. => Flexible and Re-configurable

„Moving Design to Runtime“

Challenge of learning from the perspective of the engineer

• Keep system compliant with changing requirements

Boundaries

Auto-configuration,

Learning



Automation system

Classifier QA

System

target

Image

Processing

Products /

Work pieces

Sorted

Products

Execution layer

• Image processing with classifier

• Product recognition

• Camera-guided robot

Layer

1

Adaption layer

• Observation for parameter adjustment (open loop control)

Layer

2

Learning Architecture (Example of industrial image processing)

Observer Parameter

adjustment

Optimization

• Simulation-based optimization upon models (closed loop control)

Layer

3

Optimization Simulation


Prof. Dr.-Ing. Michael Weyrich Q

uelle

: P

rof.

Christia

n M

ülle

r-S

chlo

er,

Leib

nitz U

niv

. H

annover,

Vort

rag „

Org

anic

-Com

putin

g –

Quo v

adis

?“;

htt

p:/

/ww

w.y

outu

be.c

om

/watc

h?v=

ebH

gJc4sT

kY


Citation of the DFG SPP: Organics Computing


Information technology has reached a stage from which disruptive changes of existing paradigms can be expected

Knowledge

Processing Computer based

modelling of

knowledge with

deductive

reasoning

Intelligent

data

processing sensors aggregate

data and report the

semantics of sys-

tem conditions

Autonomous

Decisions self management and

self adjustments in

the sense of

independent

action

Coordination

of the Internet

of Things new concepts for

architecture and

middleware

Human

Machine

Interface (HMI) – seamless

integration of

SmartPhone

and Apps

M2M machines

can communicate

with other devices,

new standards for

fieldbuses and

wireless networks

may come up

Seamless integration of Automation Technology

Virtual

Engineering design and

evaluation of

systems in an

augmented

reality



Architecture of Value Creation

What is the best configuration to

deliver?

Examples for Value Proposition

Which new value is created

based on which technology?

units

Le

ve

l o

f C

om

ple

xity

“Project business”

“Commodity“

Leadership in

Technology

Innovative Products with

medial complexity of

technology and cost

effectiveness

New Fuctions

Level of maturity / thorough testing

….

Adaptation and learning

Decentralized control

Efficient design

Easy to use/configure

Dependability/ safety

33 © 2014 IAS, Stuttgart University

Key Factors

Demonstration of new Technologies

Which technology provide better products or improves

the process?

Value add

trends that shape the future of automation engineering · prof. dr.-ing. michael weyrich trends...

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