Physics-Informed Machine Learning for Smart Additive Manufacturing
<|reference_start|>Physics-Informed Machine Learning for Smart Additive Manufacturing: 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).<|reference_end|>
@article{sharma2024physics-informed,
    title={Physics-Informed Machine Learning for Smart Additive Manufacturing},
    author={Rahul Sharma, Maziar Raissi, Y.B. Guo},
    journal={arXiv preprint arXiv:2407.10761},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10761},
    primaryClass={cs.LG cs.CE}
}