Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.

有效的刀具磨损监测 (TWM) 对于准确评估刀具磨损程度和及时预防性维护至关重要。现有的数据驱动监控方法主要依赖复杂的特征工程，降低了监控效率。本文提出了一种基于并行残差和堆叠双向长短期记忆 (PRes-SBiLSTM) 网络的新型 TWM 模型。首先，使用并行残差网络（PResNet）自适应地提取传感器信号的多尺度局部特征。随后，使用堆叠双向长短期记忆（SBiLSTM）网络获取与工具磨损特性相关的时间序列特征。最后，预测的刀具磨损值通过全连接网络输出。应用平滑校正方法来提高预测精度。所提出的模型经过实验验证，在不牺牲其泛化能力的情况下具有较高的预测精度。提出了一种基于PRes-SBiLSTM网络的TWM系统框架，对实际工业环境中的TWM具有一定的参考价值。