Gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism for tool wear prediction

•Soft thresholding with attention mechanisms reduces noise and enhances the generalization of the temporal convolutional network (TCN).•Integrating bidirectional gated recurrent units (BiGRU) with TCN to alleviate the problem of data feature oblivion.•Selecting features based on monotonicity, trendability, and robustness indicators enhances the accuracy of tool wear prediction.•The average mean absolute error and root mean square error are reduced by 52.72 % and 42.23 %, respectively, outperforming TCN-BiGRU. Predicting tool wear significantly improves cutting efficiency and quality in intelligent manufacturing. Traditional recurrent neural networks suffer from vanishing or exploding gradients and need to be improved prediction accuracy. Therefore, this paper proposes a gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism (TCN-BiGRU-SA). Monotonicity, robustness, and trendability indicators are used to assess time and frequency domain features. Then, evaluation results of these three indicators are weighted to identify the sensitive features with the degradation trend. The attention mechanism is proposed to adaptively adjust the soft thresholding, and the SA is added to TCN-BiGRU to remain helpful features. Finally, the proposed model is validated using an experimental dataset. Compared to the TCN-BiGRU model without the SA mechanism, the predicted results show that the average mean absolute error and root mean square error are reduced by 48.29 % and 36.39 %, respectively.