3 papers
eess.SY2025
Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency
Wenyi Liu, R. Sharma, W. "Grace" Guo +2
Digital twin (DT) enables smart manufacturing by leveraging real-time data, AI models, and intelligent control systems. This paper presents a state-of-the-art analysis on the emerg…
cs.LG2025
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…
cs.LG2024
Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of Thermal Stress Evolution in Laser Metal Deposition
R. Sharma, Y. B. Guo
Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have show…