Research on tool remaining useful life prediction algorithm based on machine learning

Abstract Tool wear during machining significantly impacts workpiece quality and productivity, making continuous monitoring and accurate prediction essential. In this context, the present study develops an efficient tool wear prediction system to enhance production reliability and reduce tool costs. It is worth noting that conventional methods, including support vector regression, autoencoders, attention mechanisms, CNNs, and RNNs, have limitations in feature extraction and efficiency. Aiming at resolving these limitations, a multiscale convolutional neural network (MDCNN)-based algorithm is proposed for predicting the remaining life of milling cutters. The algorithm uses preprocessing techniques like wavelet transform and principal component analysis for noise reduction and feature extraction. It then extracts temporal data features using convolutional layers of different scales and employs a self-attention mechanism for feature encoding. Validation on the PHM2010 milling cutter wear dataset with 10-fold cross-validation demonstrates that the MDCNN model achieves a wear prediction accuracy of 97%, a recall rate of 98%, and an F1 score of 97%. The MDCNN model effectively processes multi-band data and captures complex temporal features, confirming its efficiency and accuracy in predicting milling cutter wear and remaining service life.