Physics-Informed Machine Learning for Smart Additive Manufacturing
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).
Fields of Study & Subjects
Additive Manufacturing Materials and Processes
Machine Learning in Materials Science
Model Reduction and Neural Networks
Publication details
- Journal / source
- Procedia CIRP
- Publisher
- Elsevier BV
- Volume
- 138
- Pages
- 650–655
- Publication date
- 1 January 2026
- Language
- EN