Methodology and Experimental Verification for Predicting the Remaining Useful Life of Milling Cutters Based on Hybrid CNN-LSTM-Attention-PSA
Mengge Zhu,
Ji Zhang,
Lingfan Bu
et al.
Abstract:In modern manufacturing, the prediction of the remaining useful life (RUL) of computer numerical control (CNC) milling cutters is crucial for improving production efficiency and product quality. This study proposes a hybrid CNN-LSTM-Attention-PSA model that combines convolutional neural networks (CNN), long short-term memory (LSTM) networks, and attention mechanisms to predict the RUL of CNC milling cutters. The model integrates cutting force, vibration, and current signals for multi-channel feature extraction… Show more
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