4 citations · 8 across the 4 of their papers we have counts for
4 papers
Integrated physics-informed learning and resonance process signature for the prediction of fatigue crack growth for laser-fused alloys
Panayiotis Kousoulas, Rahul Sharma, Y. B. Guo
Fatigue behaviors of metal components by laser fusion suffer from scattering due to random geometrical defects (e.g., porosity, lack of fusion). Monitoring fatigue crack initiation…
Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing
D. Patel, R. Sharma, Y. B. Guo
Metal additive manufacturing enables unprecedented design freedom and the production of customized, complex components. However, the rapid melting and solidification dynamics inher…
Physics-Informed Machine Learning for Smart Additive Manufacturing
Rahul Sharma, Maziar Raissi, Y. B. Guo
Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data…
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics
R. Sharma, W. Grace Guo, M. Raissi +1
Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based sim…