Germany - Reliable partner for
sustainable and smart
metallurgical equipment
德国 – 冶金工业持续可信赖的伙伴
Metal and Metallurgy China
Beijing
Press Conference
17 May 2016
VDMA | Winfried Resch
Smart Engineering
» I 4.0 – a terminology of its own
Content
» The road to Industry 4.0
» Platforms
» I 4.0 in Metallurgical Engineering
Example
» Industry 4.0 – best in practice
Concluding remark
VDMA | Winfried Resch
Page 2 | May 2016
Smart Engineering
» I 4.0 – a terminology of its own
Content
» The road to Industry 4.0
» Platforms
» I 4.0 in Metallurgical Engineering
Examples
»Industry 4.0 – best in practice
Concluding remark
VDMA | Winfried Resch
Page 3 | May 2016
Self Adapting
Process Chain
Data Acquisition
Industry 4.0 – Characterized
Industry 4.0
by its own terminology
Cyber-Physical Systems
Standards
Smart Factory
Internet of Things
Picture: SMS group
Digitalization
Data Mining
VDMA | Winfried Resch
Big Data
Simulation
Industry 4.0
Smart Production
Digital Interconnection
Industrial Big Data
Modelling
Page 4 | May 2016
Source: DFKI; Graphic design: Resch
From Industry 1.0 to Industry 4.0
First
Industrial Revolution
Second
Industrial Revolution
Third
Industrial Revolution
Fourth
Industrial Revolution
Introduction of
mechanical production
facilities with the help of
water and steam power
Mass production by
division of labor with the
help of electrical energy
Use of electronic and IT
systems that further
automate production
Use of cyber-physical
systems
First mechanical loom,
1784
1750
VDMA | Winfried Resch
1800
First assembly line,
Cincinnati slaughter
house, 1870
1900
First programmable
logic controller, 1969
2000
Page 5 | May 2016
“History“ of Industry 4.0 in Germany
Platform Industry 4.0
Federal Ministries
Trade-Unions
Industry
Scientists
Associations
Platform of Associations
Industry 4.0
BITKOM, ZVEI, VDMA
+ Industry
+ Scientists
Research Union
Industry 4.0
Scientists &
First Mover from
Industry
Source: DFKI; Graphic design: Resch
01/2012
2012
Industry +
BITCOM, ZVEI, VDMA
04/2015 11/2015
04/2013
2013
2014
05/2014
VDMA-Forum
Industry 4.0
VDMA | Winfried Resch
Labs Network Industry 4.0
2015
2016
08/2014
VDMA-Metallurgy Working Committee
Industry 4.0 in Metallurgical Engineering
Page 6 | May 2016
Platform of Associations Industry 4.0
Implementation Strategy
Industry 4.0 Report of results
Key areas detected:
» Standardization & Reference architecture
» Managing complex systems
» Comprehensive broadband infrastructure
» Safety & Security of networked systems
Source: Plattform Industrie 4.0
» Work organization and design
» Training & continuing professional development
» Regulatory framework
» Resource efficiency
VDMA | Winfried Resch
Page 7 | May 2016
Exemplary result:
Reference Architecture Model Industry 4.0
> RAMI4.0 <
Source: Plattform Industrie 4.0
Implementation Strategy
Industry 4.0 Report of results
VDMA | Winfried Resch
Page 8 | May 2016
“History“ of Industry 4.0 in Germany
Platform Industry 4.0
Federal Ministries
Trade-Unions
Industry
Scientists
Associations
Platform of Associations
Industry 4.0
Research Union
Industry 4.0
BITKOM, ZVEI, VDMA
+ Industry
+ Scientists
Scientists &
First Mover from
Industry
Source: DFKI; Graphic design: Resch
01/2012
2012
Industry +
BITCOM, ZVEI, VDMA
04/2015 11/2015
04/2013
2013
2014
05/2014
VDMA-Forum
Industry 4.0
VDMA | Winfried Resch
Labs Network Industry 4.0
2015
2016
08/2014
VDMA-Metallurgy Working Committee
Industry 4.0 in Metallurgical Engineering
Page 9 | May 2016
Readiness Model – based on the following
six core areas of Industry 4.0
Industry 4.0 – Readiness Study
Self check for companies (online)
Employees
Impuls-Stiftung des VDMA, Oktober 2015
Data-driven
services
Smart
Products
VDMA | Winfried Resch
Strategy &
Organization
Smart Factory
Smart
Operations
Page 10 | May 2016
Industry 4.0 – Readiness Study (online)
- six core areas and associated tasks
Competence of the employees
Development of
professional expertise
Strategy
Investment
Employees
Strategy &
Organization
Innovation management
Digital image
Databased services
Share of sales
Data-driven
services
Machinery in operation
Smart Factory
Data usage
Share in data usage
Impuls-Stiftung des VDMA
Additional ICT funktions
IT systems
Smart
Products
Data analysis during usage phase
VDMA | Winfried Resch
Smart
Operations
Cloude usage
Autonomous processes
IT security
Exchange of information
Page 11 | May 2016
Industry 4.0 – Readiness Study (online)
- Which level reachs the company?
Employees
Data-driven
services
Impuls-Stiftung des VDMA
Smart
Products
Strategy &
Organization
Level 5
Professional
Level 4
Expert
Level 3
Experienced
learner
Level 2
Advanced
learner
Pioneers
Smart Factory
Smart
Operations
Level 1
Beginner
Absolute beginner
Newcomer
Level 0
VDMA | Winfried Resch
Observer
Page 12 | May 2016
Industry 4.0 – Readiness Study (online)
- Survey amongst VDMA members
Level
0,95 %
Employees
Data-driven
services
Impuls-Stiftung des VDMA
Smart
Products
Level
0,34 %
Strategy &
Organization
Smart Factory
Smart
Operations
S
u
r
v
e
y
Pioneers
Level
13,9
3%
45,3
Level
2%
Beginner
Level
1,51 %
Newcomer
Level
38,3
0%
VDMA | Winfried Resch
Page 13 | May 2016
Toolbox Industry 4.0
Products & Production
Guideline Industry 4.0
Guidance on the indroduction for SMEs
VDMA | Winfried Resch
Page 14 | May 2016
Toolbox Industry 4.0
- Products (examples)
Communication & Connectivity
no interface
at the product
product can
send/receive
I / O signals
product has
product has
product has
fieldbus
interfaces
industrial
ethernet
interfaces
access to www
machines having
access to
www
use of webservices
- Production (examples)
Machine-to-Machine Communication
no
communication
VDMA | Winfried Resch
fieldbus
interfaces
industrial
ethernet interfaces
Page 15 | May 2016
“History“ of Industry 4.0 in Germany
Platform Industry 4.0
Federal Ministries
Trade-Unions
Industry
Scientists
Associations
Platform of Associations
Industry 4.0
Research Union
Industry 4.0
BITKOM, ZVEI, VDMA
+ Industry
+ Scientists
Scientists &
First Mover from
Industry
Source: DFKI; Graphic design: Resch
01/2012
2012
Industry +
BITCOM, ZVEI, VDMA
04/2015 11/2015
04/2013
2013
2014
05/2014
VDMA-Forum
Industry 4.0
VDMA | Winfried Resch
Labs Network Industry 4.0
2015
2016
08/2014
VDMA-Metallurgy Working Committee
Industry 4.0 in Metallurgical Engineering
Page 16 | May 2016
VDMA Working Committee
Industry 4.0 in Metallurgical Engineering
Branches
» Metallurgical Plants and Rolling mills
» Foundry machinery
» Thermo Process Technology
Key tasks




Objective
» Positioning of metallurgical engineering within Industry 4.0
» Overall approach for the three branches
» Branch specific aspects and assets for Industry 4.0
Intention
VDMA | Winfried Resch
Overarching characteristics of Industry 4.0 for mechanical industries
Relevance of Industry 4.0 for metallurgical engineering
Existing technical/technological approaches
Development potential and demand in terms of Industry 4.0
Positioning within
Markets and
Research
Public relations
Application
incentives &
Sucess stories
Page 17 | May 2016
Industry 4.0 in Metallurgical Engineering
Main challenges
» Empirical process knowledge in the metallurgical
plant industry still more complex than its digital
counterpart
» Digital qualification of material throughput is
complex due to constantly changing physical conditions
and throughput speed
Picture: SMS group
» Long service lives of plants demand a gradual
approach to maintain functional infrastructures
VDMA | Winfried Resch
Seite 18 | May 2016
Industry 4.0
Content
»
»
»
»
I 4.0 – a terminology of its own
The road to Industry 4.0
Platforms
I 4.0 in Metallurgical Engineering
Example
» Industry 4.0 – best in practice
Concluding remark
VDMA | Winfried Resch
Page 19 | May 2016
Industry 4.0 – best in practice
scrap
BOF converter process:
Core process of the steel value chain
hot metal
Challenges
» Increasing demands on quality
Source: SMS group, TU Dortmund, Dillinger Hütte
» Steel quality is crucial for the quality
of the end product
» Climate relevance: Decarburization of hot metal
» Environmental impact: Dust and slag
VDMA | Winfried Resch
Page 20 | May 2016
Industry 4.0 – best in practice
hot metal
scrap
lime
oxygen
crude steel
slag
off-gas
dust
Additional measured data and
self-learning models needed
for process prediction & control
BOF converter process:
Core process of the steel value chain
Aim
Prediction of steel composition at the end of the
blowing process
Source : SMS group, TU Dortmund, Dillinger Hütte
» Producing steel melts with objective characteristics
T, [% C], [% P], [% Fe]
and high productivity at minimized costs
‒ Problem: prediction of temperature and steel
composition at the end of the process is not
sufficiently accurate
‒ Cause: insufficient knowledge of the properties
of the charged materials and media
VDMA | Winfried Resch
Page 21 | May 2016
Industry 4.0 – best in practice
» Principle of causality
» Mass and energy balance
» Thermodynamic equilibrium
» Phase & reaction kinetics
» Fixed model application
Source : SMS group;, TU Dortmund, Dillinger Hütte
Conventional approach:
Model(s) based on fixed rules
Data-driven hybrid model
1 0 1 0 0 0 0 1 0 0
0 1 0 0 0 1 1 0 1 1
0 1 1 0 0 0 0 1 0 1
0 0 0 1 0 1 1 1 1 0
0 0 1 0 0 0 1 0 0 0
0 0 1 0 1 1 0 1 0 0
0New
0 1 1 1 1 0 0 0 1
1 0 1approach:
1 0 0 0 0 0 0
Data-based
1 0 1 0 0 1 model(s)
1 1 0 0
0 0 1 0 1 1 0 0 0 1
VDMA | Winfried Resch
1
0
0
1
1
1
1
1
1
1
1
0
0
0
1
1
1
0
1
1
1
0
0
0
0
0
1
0
0
1
0
1
1
0
0
1
1
1
0
0
0
1
0
1
0
0
1
1
0
0
1
0
1
1
1
1
0
0
1
0
1
0
1
0
1
1
0
1
1
1
1
0
1
0
0
1
1
1
0
1
0
1
0
1
0
1
0
0
0
1
0
1
1
1
1
0
1
1
1
1
» Principle of correlation
» Process data (measured, empirically)
» Flexible adjustment through
self-analysis, self-learning,
self-optimization functions
Page 22 | May 2016
Industry 4.0 – best in practice
RapidMiner© – Data Analysis during the Process
Source: SMS group, TU Dortmund, Dillinger Hütte
» Measurement / observance of
126 process parameters leading to
31 characteristics
Industrial Big Data
VDMA | Winfried Resch
» Extraction of 20 relevant process
characteristics by combination of time
series and single values of measured data
Page 23 | May 2016
Industry 4.0 – best in practice
Industrial Big Data
Intelligent data-driven models for endpoint prediction at the BOF converter
Drifting of prediction errors caused, e.g., by aging of
brick lining or wear of the lance and bottom nozzles
Start:
Model A
Prediction error
Source: SMS group, TU Dortmund, Dillinger Hütte
Learning:
new Model B
A
A
VDMA | Winfried Resch
B
A
B
Accepted range
Lance
Campaign 1
Lance
Campaign 2
Quality of the model is
continuously monitored
 self-organized learning and
applying new models
Application:
B modified
Application:
new Model B
Lance
Campaign 3
time
Defined changes to the process
or installation are performed
 directed learning and applying
Page 24 | May 2016
Industry 4.0 – best in practice
Summary: Intelligent data-driven models for
endpoint prediction at the BOF converter
» Efficient modeling for different applications in
steel production chain
» Optimization of the process chain based on
consistent data and process models
» Offline process simulation on the PC, thereby
allowing rapid adaptation to customer needs
» Approx. € 100,000 / a savings with 1°K better
accuracy in steel temperature prediction
www.sms-group.com
VDMA | Winfried Resch
Page 25 | May 2016
Concluding remark
Concluding remark
VDMA | Winfried Resch
Page 26 | May 2016
Contact
Winfried RESCH
[email protected]
感谢您的关注 !
Thank you
Thank you
for your attention!
VDMA |
Page 27 | May 2016