Industrial Transfer Learning: a research area of the chair for technologies and management of digital transformation from the university of Wuppertal, Germany. For more information, see here: https://www.tmdt.uni-wuppertal.de/de

1. Industrial Transfer Learning

2. Introduction to Industrial Transfer Learning

3. 3 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Motivation
Industrial Transfer Learning
Challenges for machine learning in manufacturing
Dynamic processes  high training effort
Insufficient data  representative and reliable data required
!
Machine Learning in Manufacturing
Process Control Self-Optimization
Predictive Quality Predictive Maintenance
AutomationDecision Support

4. 4 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Challenges for Machine Learning in Production
Industrial Transfer Learning
Small quantity
High variation
High costs
Test Environment
High quantity
Little variation
Highly optimized
Running Production
Simplification
High variation
Low costs
Simulation
(Experiments)
 One key requirement of successful ML: representative and reliable data basis
 Main data sources in production have advantages and disadvantages regarding costs and data quantity
How to learn from different domains?

5. 5 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Challenges for Machine Learning in Production
Industrial Transfer Learning
Process variations lead to high learning effort for AI
e.g. new product, other material, tool change, new machine
How to overcome process variations?
Product A
New Data
& Training
Product B Product C
New Data
& Training
New Data
& Training

6. 6 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Transfer Learning – An Emerging Paradigm
Industrial Transfer Learning
What is Transfer Learning?
Traditional ML: learning
a problem from scratch
Transfer Learning: use of
existing knowledge
Result: faster learning process
with less target data
[1]
Source Tasks
Model Model
Target Task
Knowledge
“Transfer learning will be the next driver of ML success.”
Andrew Ng, NIPS 2016 keynote
[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2010): 1345-1359.

7. 7 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Transfer Learning – State of the Art
Industrial Transfer Learning
Use Cases of Deep Transfer Learning
Robotics
Pretraining in
simulation for
grasping and
manipulation
Self-Driving Cars
Use of simulation
environment to train
artificial intelligence
Computer Vision
Transfer of pattern
recognition (e.g.
edges, objects) to
new images
Music
Classification
Use of large datasets
for classifying music
genre
Natural Language
Processing
Use of pretrained
language models for
specific NLP tasks

8. 8 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Transfer Learning – A Definition
Industrial Transfer Learning
In the field of production, industrial transfer learning refers to machine learning methods and
techniques that make use of source data from different production process domains or process
variations with the goal to create robust, accurate and data efficient models for a certain target task.
Real Machine
Pre-production
Expert Knowledge
Simulation
Process domain
Product
Material
Tool
Machine
Process variation

9. Industrial Applications
Simulation to Reality Transfer for Predictive Quality

10. 10 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Predictive Quality in Injection Molding
Simulation to Reality Transfer for Predictive Quality
Supporting process designers in the initial set-up of a machine by predicting quality criteria
from machine parameters
Increasing data efficiency by transfer learning from simulation to real world
Conducting design of experiments on real machine and simulation with six parameters
Cooling TimeCavity Temperature Melt Temperature
Injection Time
Holding pressure level
Holding pressure time
Quality
(part weight)
Plate
Specimen

11. 11 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Bridging the Reality Gap
Simulation to Reality Transfer for Predictive Quality
Machine
Parameters
Pretraining
(simulation)
Part weight
Finetuning
(real data)
Transfer Learning
 Pretraining in simulation
(Cadmould 3D-F)
 Finetuning of the network
Model Training
 Neural network with two hidden
layers with 40 neurons
 Activation function: tanh

12. 12 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Successful Transfer
Simulation to Reality Transfer for Predictive Quality
Pretrained from
simulation
Transfer
0 5000 10000 15000 20000 25000
Ohne TL
Mit TL
Number of Training Iterations
Baseline
Transfer
Use of simulation data improves prediction models for
real process
Improvement in accuracy by factor of 3
Reduction of learning effort (iterations) by 80%
Reduction of Training Effort
Increasing Data Efficiency
-1
-0.6
-0.2
0.2
0.6
1
1 10 20 30 40 50 60
Performance
Number of Real Experiments
Ohne TL
Mit TLTransfer
Without Transfer

13. 13 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Successful Transfer
Simulation to Reality Transfer for Predictive Quality
Simulation AI-Model
Training
Trigger
Process
Control
Adjustment
 AI bridges the gap between simulation and
real manufacturing process
 Use for automated design in production line
 In case of uncertain predictions:
 Automatic triggering of new experiments
in simulation
 Transfer of newly gained knowledge to
real process
Continuous improvement of model
by new simulated experiments

14. Industrial Applications
Continual Learning of a Predictive Quality Model

15. 15 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Predictive Quality in Injection Molding
Continual Learning of a Predictive Quality Model
Predicting quality criteria from machine
parameters by means of a neural network
Cooling TimeCavity Temperature Melt Temperature
Injection Time
Holding pressure level
Holding pressure time
Quality
(Deformation)
Production of a new product variants
Changes in geometry and process behavior
 Predictions no longer work
 Requires training of a new prediction model
Difference of quality for different products

16. 16 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Use of Previous Knowledge for Transfer
Continual Learning of Predictive Quality Model
Product 1 Product 2 Product 3 Product 4
Transfer Transfer Transfer
Amount of data decreases Learning capability increases
Learning without forgetting

17. 17 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Incremental Learning without Forgetting
Continual Learning of Predictive Quality Model
process specific product specific
Product 1
…
Finetuning Retuning
Product 2 Product 3
Learning
without
forgetting

18. 18 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Improving Efficiency and Learning
Continual Learning of Predictive Quality Model
70
80
90
100
1st 2nd 3rd 4th 5th 6th
Continual Learning
Learning from Scratch
Products
Performance
0
10
20
30
40
50
60
70
80
1st 2nd 3rd 4th 5th 6th
Continual Learning Learning from Scratch
Products
#TrainingData
Improved Performance
 Continual learning approach keeps
up performance
 Traditional approach becomes worse
with every product
Improved Data Efficiency
 Number of required training data is
reduced for every product
 Prediction model can generalize
better to new parts

19. Industrial Applications
Sim2Real Transfer for Reinforcement Learning in Robotics

20. 20 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Reinforcement Learning
 AI agent learns by means of interactions with its environment
– Agent observes state
– Agent chooses action
– Environment issues reward
 Actor-critic architecture
– Critic: learns the action-value function
– Actor: specifies the current policy
 Deep Deterministic Policy Gradient (DDPG).
– Used for a number of continuous control tasks in simulated environments
Sim2Real Transfer for Reinforcement Learning in Robotics
Automated Trial-and-Error by Learning AI Model

21. 21 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Use of DDPG in the Real World
Sim2Real Transfer for Reinforcement Learning in Robotics
 The wire loop game as an easy-to-control sandbox scenario.
– State: camera images, Action: three degrees of freedom (forward, sideways, rotation),
Reward: contact between fork and wire
camera images
image processing (CNN) decision making (FCNN)
execution of motion
current signal
High training effort on real industrial robot!!

22. 22 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Transfer Learning with Domain Randomization
Sim2Real Transfer for Reinforcement Learning in Robotics
Training in real robotic environment is time consuming and costly
Solution: transfer learning from simulation to the real world
Creating robust AI by randomizations in simulation
Randomizations: Camera position and rotation, color, texture, noise

23. 23 Industrial Transfer Learning
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Results
Sim2Real Transfer for Reinforcement Learning in Robotics
Input
Image
Without
Transfer
Transfer
Learning
Higher Reliability
With transfer: attention of agent lies
on correct areas in image (red area)
Improving performance: number of errors in real
environment is drastically reduced
Cost savings: reduction of real iterations with
robot for training by 70%

24. Your Contact Person:
Hasan Tercan, M.Sc.
Tel: +49 (0)202 439 1153
tercan@uni-wuppertal.de
Chair for Technologies and Management of Digital Transformation
Univ. Prof. Dr. Ing. Tobias Meisen
www.tmdt.uni-wuppertal.de
Campus Freudenberg
Rainer-Gruenter-Str. 21
D-42119 Wuppertal
Germany
University of Wuppertal
School of Electrical, Information and Media Engineering