Tool wear prediction in milling process using physics-informed machine learning and thermo-mechanical force model with monitoring applications

Abstract

Accurate wear estimation of milling tools is critical for enhancing the productivity and reliability of machining processes, ensuring consistent product quality while minimizing unexpected tool failure, downtime and machining costs. Traditional approaches, often based on pure experimental and data-driven machine learning (ML) methods, demand extensive, costly wear testing to gather the necessary datasets, which limits their utility in practical industrial monitoring. To address this gap, this work presents a novel physics-informed machine learning (PIML) approach of wear estimation by integrating analytical models with ML techniques. The PIML model utilizes a wear-inclusive thermo-mechanical model to estimating cutting forces considering flank wear and edge forces, with special focus on its adaptation to milling operations and addressing the complexities of milling dynamics. The methodology is demonstrated on Steel 1050, a widely used medium-carbon steel alloy in industrial machining applications. As shown by the results, this hybrid model shows high predictive accuracy, achieving R² values exceeding 98 % for force prediction and 95 % for tool wear estimation, with corresponding RMSE values below 14 N and 8 µm, respectively. Notably, the use of the PIML framework improved tool wear prediction accuracy by over 16 % compared to using ML alone. Another important finding is the significant role of edge forces under severe wear conditions, with their contribution to average cutting forces increasing from 40 % to 57 % at low feed rates, and from 27 % to 45 % at higher feed rates. Using this enhanced model, a simulation-based dataset was generated to train an inverse ML model for estimating tool wear considering milling forces and cutting parameters. The inverse ML model exhibited robust predictive performance, offering a practical and accurate solution for tool wear estimation. This study emphasizes the promising potential of integrating thermo-mechanical model with ML algorithms in machining applications, establishing a foundation of tool wear condition monitoring through milling force data. The presented approach can contribute to enhanced process control, optimized tool usage, and reduced operational costs. Furthermore, it supports the transition to Industry 4.0 by enabling automation and unsupervised manufacturing, where real-time tool wear monitoring and adaptive control can be achieved with minimal human intervention, driving more intelligent and efficient manufacturing systems.