Physics-Informed Machine Learning for Smart Additive Manufacturing

• URL: http://arxiv.org/abs/2407.10761v1
• Date: Mon, 15 Jul 2024 14:40:24 GMT
• Title: Physics-Informed Machine Learning for Smart Additive Manufacturing
• Authors: Rahul Sharma, Maziar Raissi, Y. B. Guo,
• Abstract summary: 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)
• Score: 2.3091320511105353
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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