Qin Yongrui, Li Jiangfeng, Zhang Chenxi, Zhao Qinpei, Ma Xiaofeng
Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia.
School of Software Engineering, Tongji University, Shanghai 201804, China.
Entropy (Basel). 2022 Nov 28;24(12):1733. doi: 10.3390/e24121733.
The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results.
刀具磨损状态的智能监测与磨损预测是影响现代机械工业智能化发展的重要因素。许多学者采用深度学习方法在刀具磨损预测方面取得了一定成果。然而，由于信号数据的不稳定性和变异性，一些神经网络模型可能会出现层间梯度衰减。大多数方法主要关注输入数据的特征选择，却忽略了不同特征对刀具磨损的影响程度。为了解决这些问题，本文提出了一种用于刀具磨损预测的双阶段注意力模型。设计了一种CNN-BiGRU-注意力网络模型，该模型引入自注意力来提取深层特征并体现更重要的特征。使用IndyLSTM构建稳定网络以解决层间梯度衰减问题。此外，在网络中添加注意力机制以获取输出序列的重要信息，从而提高预测精度。针对干式铣削加工中的刀具磨损预测进行了实验研究，以证明该方法的可行性。通过与回归预测评估指标进行实验对比分析，证明了所提方法能够有效表征刀具磨损程度，减少预测误差，并取得良好的预测结果。
