A Physics-Informed Machine Learning Framework to Accurately Predict Tool Wear and Surface Integrity

Abstract

Accurate prediction of tool wear and surface integrity assessment are key challenges in modern manufacturing, which directly affect manufacturing productivity, cost efficiency, and product quality. Current physics models are not flexible enough for different cutting conditions, and data-driven models need large amounts of labeled data and cannot be based on basic physical laws. This paper introduces a detailed mathematical model that combines Physics-Informed Machine Learning (PIML) techniques—such as Physics-Informed Neural Networks (PINNs), physics-informed Gaussian process regression and hybrid architectures—to simulate tool wear and surface integrity in machining processes. The framework integrates cutting mechanics, thermomechanical state variables and wear kinetics systematically into data-driven models to allow prediction with limited and noisy data from the industry. This discussion covers the latest advances in physics-informed machine learning (PIML) techniques, compares hybrid methods to pure data-driven and physics-based approaches, and highlights research gaps in real-time tool condition monitoring and generalization across cutting parameters. Key results show that a physics-informed model can attain prediction accuracy of R² ≥ 0.92–0.97, underlining the physical consistency, and with fewer training samples than conventional neural networks. Open challenges in uncertainty quantification and on edge devices are discussed, and practical implementations for Industry 4.0 smart manufacturing and remaining useful life estimation are discussed. This work provides a common theoretical and practical framework for physics-informed machine learning in manufacturing systems for various cutting processes and materials.