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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Physics-Informed Machine Learning of Thermal Stress Evolution in Laser Metal Deposition
Author(s) Rahul Sharma, Yuebin Guo
On-Site Speaker (Planned) Rahul Sharma
Abstract Scope Rapid laser scanning generates a steep temperature gradient in the heat-affected zone in laser additive manufacturing. The gradient leads to very high thermal stresses that evolve into residual stresses after the component cools down. Data-driven models, such as machine learning (ML), offer an alternative to traditional physics-based simulations for calculating the thermal stress evolution. However, ML models require a large, labeled training dataset, which makes them computationally inefficient. The "black box" nature of ML models makes it difficult to interpret the results. Additionally, the data-driven models do not effectively use governing physical laws to make them data-efficient. This study aims to develop a physics-informed machine learning model that can predict thermal stresses during laser scanning without requiring any labeled training dataset. A case study has been conducted to demonstrate the predictive capability of the PIML method and examine the evolution of thermal stresses in a laser metal deposition process.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Machine Learning, Modeling and Simulation

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