Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation
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 canno...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1742 |
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| author | Zhigang Cai Wangyang Li Jianxin Song Hongyu Jin Hongya Fu |
| author_facet | Zhigang Cai Wangyang Li Jianxin Song Hongyu Jin Hongya Fu |
| author_sort | Zhigang Cai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c092f0ae37094cbc879b639a36f25bb2 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c092f0ae37094cbc879b639a36f25bb22025-08-20T01:48:49ZengMDPI AGSensors1424-82202025-03-01256174210.3390/s25061742Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain AdaptationZhigang Cai0Wangyang Li1Jianxin Song2Hongyu Jin3Hongya Fu4School of Mechatronic Engineering, Harbin Institute of Technology, Harbin 150001, ChinaInspur Genersoft Co., Ltd., Jinan 250101, ChinaSchool of Mechatronic Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Mechatronic Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Mechatronic Engineering, Harbin Institute of Technology, Harbin 150001, ChinaAccurately 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.https://www.mdpi.com/1424-8220/25/6/1742tool wear state identificationtransfer learningmulti-source unsupervised domain adaptionvarying cutting parameters |
| spellingShingle | Zhigang Cai Wangyang Li Jianxin Song Hongyu Jin Hongya Fu Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation Sensors tool wear state identification transfer learning multi-source unsupervised domain adaption varying cutting parameters |
| title | Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation |
| title_full | Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation |
| title_fullStr | Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation |
| title_full_unstemmed | Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation |
| title_short | Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation |
| title_sort | tool wear state identification method with variable cutting parameters based on multi source unsupervised domain adaptation |
| topic | tool wear state identification transfer learning multi-source unsupervised domain adaption varying cutting parameters |
| url | https://www.mdpi.com/1424-8220/25/6/1742 |
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