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arXiv · 2407.10761

Physics-Informed Machine Learning for Smart Additive Manufacturing

Rahul Sharma·Maziar Raissi·Y. B. Guo
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Abstract

Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML's "black box" nature has presented a challenge to interpreting its outcomes. On the other hand, governing physical laws are not effectively utilized to develop data-efficient ML algorithms. To leverage the advantages of ML and physical laws of advanced manufacturing, this paper focuses on the development of a physics-informed machine learning (PIML) model by integrating neural networks and physical laws to improve model accuracy, transparency, and generalization with case studies in laser metal deposition (LMD).

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Rahul Sharma, Maziar Raissi, Y. B. Guo. 2024-07-15. Physics-Informed Machine Learning for Smart Additive Manufacturing. https://arxiv.org/abs/2407.10761

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