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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI-Enabled Materials Processing: Integrating Accelerated Experimental Workflows and Processing-Aware Machine Learning
Presentation Title Machine Learning for Thermographic Feature Extraction in Multi-Material Directed-Energy Deposition
Author(s) Rujing Zha, Jannes Heidemann, Rowan Rolark, Lee Marvin Hartung, Jian Cao
On-Site Speaker (Planned) Rujing Zha
Abstract Scope Closed-loop control of the melt pool temperature additive manufacturing promises bringing part-to-part consistency to the directed-energy deposition additive manufacturing process. However, the confidence of model-based feedback controllers remains uncertain due to the tenuous connection between the cleanly simulated melt pool temperature and highly dynamic real thermographic signals, especially in the case of fabricating multi-material components. In this work, the feasibility of single-point and imaging pyrometers as a signal source to feedback controllers is evaluated by comparing thermographic signals from multi-track, multi-layer deposition experiments made using IN625 and SS316. Machine-learning based techniques are critically evaluated in their ability to distill the experimentally observed melt pool field into usable features for feedback control, as well as their ability to approximate high-degree-of-freedom models for model-based control.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Machine Learning, High-Temperature Materials

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