Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

Abstract

Machining tools are a critical component in machine manufacturing, the life cycle of which is an asymmetrical process. Extracting and modeling the tool life variation features is very significant for accurately predicting the tool’s remaining useful life (RUL), and it is vital to ensure product reliability. In this study, based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), a tool wear evolution and RUL prediction method by combining CNN-BiLSTM and attention mechanism is proposed. The powerful CNN is applied to directly process the sensor-monitored data and extract local feature information; the BiLSTM neural network is used to adaptively extract temporal features; the attention mechanism can selectively study the important degradation features and extract the tool wear status information. By evaluating the performance and generalization ability of the proposed method under different working conditions, two datasets are applied for experiments, and the proposed method outperforms the traditional method in terms of prediction accuracy.