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).
@article{arxiv.2407.10761,
title = {Physics-Informed Machine Learning for Smart Additive Manufacturing},
author = {Rahul Sharma and Maziar Raissi and Y. B. Guo},
journal= {arXiv preprint arXiv:2407.10761},
year = {2024}
}
Comments
6 pages, 7 figures, 18th CIRP Conference on Intelligent Computation in Manufacturing Engineering