The project, entitled  “Collaborative Research: Specific Energy-Based Prognosis for Machining Surface Integrity through Integration of Process Physics and Machine Learning” and  funded at $350K for the period of 8/1/2021 – 7/31/2024, aims at improving the fundamental understanding of the compounding effects of machining process parameters on the surface integrity (such as roughness, residual stress, and microstructure) of the machined parts. This will be achieved by integrating metal cutting physics with machine learning (ML) models to enable improved transparency and interpretability of the ML models for accurate, reliable, and physics-guided surface integrity prediction.