Data-mechanism Dual-model Fusion Method for Tool Wear Monitoring

doi:10.3901/JME.260104

Abstract

Abstract: To address the limitations of data-driven methods lacking physical interpretability and mechanistic models' insufficient real-time capability in tool wear monitoring, a novel predictive algorithm is proposed by fusing data-driven and mechanism-based models. First, a multi-scale spatiotemporal feature extraction network for vibration signals is constructed, capturing the dynamic characteristics of tool degradation by integrating multi-scale convolutional neural network and Transformer model. A finite element simulation model for the milling process is established, and the Usui wear rate equation is used to simulate the tool’s geometric evolution pattern. A strong tracking Kalman filter algorithm is designed, where the mechanism simulation results serve as the prior for state transition, and data prediction values are used for observation updates, enabling dynamic optimal estimation of tool wear. Experimental results demonstrate that, compared to single-model-driven prediction methods, the proposed data-mechanism dual-model fusion prediction algorithm significantly improves the accuracy and robustness of tool wear monitoring.

Key words: tool wear monitoring, data and mechanism fusion model, strong tracking Kalman filter, multi-scale spatiotemporal feature extraction network, finite element

CLC Number:

TH165

CHEN Anhang, ZHANG Haixia, YUAN Dongfeng, HAN Qiaojian, LI Na, CAO Feng. Data-mechanism Dual-model Fusion Method for Tool Wear Monitoring[J]. Journal of Mechanical Engineering, 2026, 62(4): 37-51.

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