A Dual-Stage Attention-Based LSTM Neural Network for Tool Remaining Useful Life Prediction

As an indispensable part of the mechanical manufacturing system, the wear state of the tool will affect the productivity and machining quality. Therefore, it is of great significance to accurately predict its remaining useful life (RUL). This paper proposes a non-autoregressive dual-stage attention-based long-short term memory network (DA-LSTM) for tool RUL prediction. The dual-stage attention mec...