For the WATIFY seminar 20 april 2018 I presented this first builfd of a Digital Twin for a 3D Printer. Advanced manufacturing is the use of innovative technology to improve products or processes. An important innovative technology is additive manufacturing or 3D printing. In this webinar some practical examples are given how digitization is used to improve 3D printing: 1) e-supply chain tools for additive manufacturing,  2) automated root cause analyses of printing defects, 3) use of deep learning towards Zero Defects.

1. ADVANCED MANUFACTURING WITH 3D PRINTING
DIGITALIZATION: A KEY ELEMENT FOR THE NEXT LEVEL OF AM
Jan Eite Bullema | Senior Scientist | TNO
jan_eite.bullema@tno.nl

2. AM SYSTEMS
The AMSYSTEMS Center is a joint innovation center of TNO and the High Tech
Systems Center of Eindhoven University of Technology (TU/e HTSC) to
accelerate (new ways of) additive manufacturing in diverse industries

3. CONTENT
- e-supply chain tools for additive manufacturing
- automated root cause analyses of printing defects
- use of deep learning towards Zero Defects

4. MANSYS
The aim of the ManSYS project (2013 – 2016) was to develop a complete
decision making system and robust supply chain management system for metal
additive manufacturing; especially for medical / aviation / dental
jan_eite.bullema@tno.nl

5. MANSYS: E-SUPPLY CHAIN FOR QUALITY PARTS
The aim of the ManSYS project (2013 – 2016) was to develop a complete
decision making system and robust supply chain management system for metal
additive manufacturing; especially for medical / aviation / dental
Three key elements were defined
-Decision support software
-Supply chain management
-Optimization of new products
jan_eite.bullema@tno.nl

6. SUPPLY CHAIN MANAGEMENT AND DECISION MAKING SOFTWARE
Logistics
End-User
Decision
making
software
Material
Supplier
Service
Bureau
ManSYS
office
ManSYS
Cloud
ManSYS
Webservice
jan_eite.bullema@tno.nl

7. E-SUPPLY CHAIN TOOLS FOR ADDITIVE MANUFACTURING
jan_eite.bullema@tno.nl

8. OPTIMIZING OF NEW PRODUCTS/ ASTM F42 JG TEST ARTIFACTS

9. ICT TOOLS FOR AM INTEGRATION
Additive Manufacturing is a vital enabler for Industry 4.0
jan_eite.bullema@tno.nl

10. ICT TOOLS FOR AM INTEGRATION
Modelling and Simulation
Design
Materials
Build Process
Post Processing
Product
End of Life
Cloud Enabled
jan_eite.bullema@tno.nl

11. ICT TOOLS FOR AM INTEGRATION
AM Stage Software Tool
Modelling and Simulation SolidWorks 2018, Autodesk Netfabb, Digimat-AM, SimcenterTM
Design SolidWorks 2018, Autodesk Netfabb, Magics, Digimat-AM
Materials Digimat-AM, Senvol Database
Build Process SolidWorks 2018, Autodesk Netfabb
Post Processing Simufact
Product Magics Reporting, TeamcenterTM
End of Life TeamcenterTM
Cloud Enabled SolidWorks 2018, Autodesk Netfabb, Cloud, Mindsphere
Autodesk, Dassault, Materialise, Siemens are important solution providers
jan_eite.bullema@tno.nl

12. CONTENT
- e-supply chain tools for additive manufacturing
- automated root cause analyses of printing defects
- use of deep learning towards Zero Defects

13. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

14. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

15. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

16. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

17. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

18. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

19. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

20. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

21. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

22. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

23. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

24. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

25. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

26. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

27. AUTOMATED ROOT CAUSE ANALYSES OF PRINTING DEFECTS
jan_eite.bullema@tno.nl

28. CONTENT
- e-supply chain tools for additive manufacturing
- automated root cause analyses of printing defects
- use of deep learning towards Zero Defects

29. USE OF DEEP LEARNING TOWARDS ZERO DEFECTS
With Deep Learning models are generated from (Big) Data
In general: the more data available the better the models perform
A caveat: developing useful models requires process knowledge
jan_eite.bullema@tno.nl

30. USE OF DEEP LEARNING TOWARDS ZERO DEFECTS
With Deep Learning models are generated from (Big) Data
In general: the more data available the better the models perform
A caveat: developing useful models requires process knowledge
Product Control
Process Control
Design Control
jan_eite.bullema@tno.nl

31. DEEP LEARNING FOR PRODUCT CONTROL
With deep learning defect classification can be implemented
jan_eite.bullema@tno.nl

32. DEEP LEARNING FOR PRODUCT CONTROL
With deep learning defect classification can be implemented
jan_eite.bullema@tno.nl

33. DEEP LEARNING FOR PRODUCT CONTROL
With deep learning defect classification can be implemented
jan_eite.bullema@tno.nl

34. DEEP LEARNING FOR PRODUCT CONTROL
With deep learning defect classification can be implemented
jan_eite.bullema@tno.nl

35. DEEP LEARNING FOR PRODUCT CONTROL
With deep learning defect classification can be implemented
jan_eite.bullema@tno.nl

36. DEEP LEARNING FOR PROCESS CONTROL
A simple form of process control with Deep Learning is anomaly detection,
ultimately the goal is closed loop process control with Deep Learning
jan_eite.bullema@tno.nl

37. DEEP LEARNING FOR PROCESS CONTROL
Example Anomaly detection for predictive maintenance on a motor
https://www.oreilly.com/ideas/anomaly-detection-with-apache-mxnet

38. DEEP LEARNING FOR PROCESS CONTROL
Anomaly predictions on a test data set to simulate failure
https://www.oreilly.com/ideas/anomaly-detection-with-apache-mxnet

39. DEEP LEARNING FOR PROCESS CONTROL
Anomaly detection can help the 3D printing process
jan_eite.bullema@tno.nl

40. DEEP LEARNING FOR DESIGN CONTROL
Currently some very basic design rules are implemented.
With Deep Learning it is possible to not only validate a design but also optimize
a design for optimal 3D printing
jan_eite.bullema@tno.nl

41. DEEP LEARNING FOR DESIGN CONTROL
Design Control will enable design of optimized products
Ultimately coupling (1) manufacturing, (2) field performance and (3) user
experience
https://www.autodesk.com/solutions/generative-design
Autodesk Generative Design is an example
how 3D product designers can be helped
in optimizing product design for 3D printing
jan_eite.bullema@tno.nl

42. CONCLUSION
Advanced Manufacturing with 3D Printing
Digitalization: A key element for the next level of AM

43. THANK YOU FOR YOUR ATTENTION
Innovation by TNO and TU/e High Tech Systems Center
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