Physics-Informed Machine Learning for Smart Additive Manufacturing
This work addresses the need for more interpretable and data-efficient models in smart additive manufacturing, though it appears incremental as it combines existing neural networks with physical laws.
The paper tackled the challenge of interpreting data-driven machine learning models and underutilizing physical laws in additive manufacturing by developing a physics-informed machine learning model that integrates neural networks with physical laws, resulting in improved accuracy, transparency, and generalization in laser metal deposition case studies.
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).