About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Autonomous Path Planning in Additive Processes Using Semi-supervised Machine Learning |
Author(s) |
Sean P. Donegan, James Collins, Edwin Schwalbach |
On-Site Speaker (Planned) |
Sean P. Donegan |
Abstract Scope |
As additive manufacturing (AM) continues to expand in use cases across the industrial base, increasing focus is being placed on development of novel process optimization techniques that drive improvements in throughput and performance. An area of major untapped potential is local optimization of the toolpath used in the additive process: tailored, part-specific process planning that yields desired properties as a function of geometry. In major metals AM processes such as laser powder bed fusion, scan trajectories are usually designed under simple metrics that ensure complete coverage while minimizing processing time. We propose a framework for autonomous design of AM toolpaths using semi-supervised machine learning coupled to a physics-based process model. By abstracting elements of process design into multiple “agents,” we demonstrate a model system that can generate toolpaths for arbitrary geometries from minimal training. We showcase the framework via application to several synthetic test cases. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Additive Manufacturing, Modeling and Simulation |