Doctoral Thesis: Development of physics-informed machine learning model for machining processes

Physics-informed machine learning is a rapidly growing area within Scientific Machine Learning (SciML) that integrates physical laws with machine learning and deep learning techniques. This integration is inherently bi-directional: physical principles, such as conservation laws, governing equations, and domain-specific knowledge, are embedded into artificial intelligence (AI) models to enhance their accuracy, robustness, and interpretability, while AI methods can, in turn, assist in identifying governing equations and unknown model parameters, thereby deepening our understanding of complex physical systems. As a result, physicsinformed machine learning enables high-fidelity predictions with reduced data requirements and provides efficient tools for solving challenging differential equations.
The objective of this PhD project is to leverage physics-informed machine learning models for their application in machining processes, including turning, drilling, and broaching. These models w...