Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network

Abstract

Tool wear prediction can ensure product quality and production efficiency during manufacturing. Although traditional methods have achieved some success, they often face accuracy and real-time performance limitations. The current study combines multi-channel 1D convolutional neural networks (1D-CNNs) with temporal convolutional networks (TCNs) to enhance the precision and efficiency of tool wear prediction. A multi-channel 1D-CNN architecture is constructed to extract features from multi-source data. Additionally, a TCN is utilized for time series analysis to establish long-term dependencies and achieve more accurate predictions. Moreover, considering the parallel computation of the designed architecture, the computational efficiency is significantly improved. The experimental results reveal the performance of the established model in forecasting tool wear and its superiority to the existing studies in all relevant evaluation indices.