About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Supervised Machine Learning for Collision Weld Process Optimization |
Author(s) |
Blake D. Barnett, Glenn Daehn |
On-Site Speaker (Planned) |
Blake D. Barnett |
Abstract Scope |
Statistical process control (SPC) and artificial intelligence/machine learning (AI/ML) have enjoyed widespread successful use in fusion and solid-state welding process control. However, collision welding techniques such as explosive welding (EXW), magnetic pulse welding (MPW), laser impact welding (LIW), and vaporizing foil actuator welding (VFAW) cannot be subject to in-process control due to the microsecond timescales over which acceleration and impact occur. Machine learning techniques offer accessible pathways to understanding collision welding results and identifying key process variable-weld performance correlations across multiple welding technologies. The feasibility of this concept was demonstrated through the application of supervised learning algorithms to perform classification of simulated collision welds as inside or outside of classical analytic welding windows with prediction accuracy above 80%. Top performing model-trained algorithms were also tested against literature data to evaluate real-world performance. |
Proceedings Inclusion? |
Undecided |