Cai Zhigang, Li Wangyang, Song Jianxin, Jin Hongyu, Fu Hongya
School of Mechatronic Engineering, Harbin Institute of Technology, Harbin 150001, China.
Inspur Genersoft Co., Ltd., Jinan 250101, China.
Sensors (Basel). 2025 Mar 11;25(6):1742. doi: 10.3390/s25061742.
Accurately identifying tool wear states with variable cutting parameters can improve machining quality and efficiency. However, existing wear state recognition methods based on unsupervised domain adaptation mostly employ the knowledge transfer learning strategy in a single source domain. They cannot fully utilize the sensor data distribution information of multiple cutting parameters, hindering recognition performance improvement. Thus, this paper proposes a wear-state recognition method for variable cutting parameters based on multi-source unsupervised domain adaptation. First, non-stationary Transformer encoders extract non-stationary common features; then, sliced Wasserstein distance-based domain-specific feature distribution alignment and classifier output alignment scale down the domain shift and make multi-domain distribution synchronous alignment less complex. Finally, the milling experiments with variable cutting parameters are conducted to validate the recognition performance of the proposed method.
准确识别具有可变切削参数的刀具磨损状态可以提高加工质量和效率。然而，现有的基于无监督域自适应的磨损状态识别方法大多在单一源域中采用知识迁移学习策略。它们无法充分利用多个切削参数的传感器数据分布信息，阻碍了识别性能的提升。因此，本文提出了一种基于多源无监督域自适应的可变切削参数磨损状态识别方法。首先，非平稳Transformer编码器提取非平稳公共特征；然后，基于切片Wasserstein距离的特定域特征分布对齐和分类器输出对齐减少了域偏移，并使多域分布同步对齐的复杂度降低。最后，进行了可变切削参数的铣削实验，以验证所提方法的识别性能。
