Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method
Real-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize tool conditions. On the other hand, it is costly...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/8/616 |
| _version_ | 1797443908427317248 |
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| author | Zhimeng Li Wen Zhong Yonggang Shi Ming Yu Jian Zhao Guofeng Wang |
| author_facet | Zhimeng Li Wen Zhong Yonggang Shi Ming Yu Jian Zhao Guofeng Wang |
| author_sort | Zhimeng Li |
| collection | DOAJ |
| description | Real-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize tool conditions. On the other hand, it is costly to obtain sufficient test data on corner milling for most of the current pattern recognition methods, which are based on the supervised method. In this work, we propose a time-frequency intrinsic feature extraction strategy of acoustic emission signal (AEs) to construct a cutting condition-independent method for tool wear monitoring. The proposed new feature-extraction strategy is used to obtain the tool wear conditions through the intrinsic information of the time-frequency image of AEs. In addition, an unsupervised tool condition recognition framework, including the unsupervised feature selection, the clustering based on adjacent grids searching (CAGS) and the density factor based on CAGS, is proposed to determine the relationship between tool wear values and AE features. To test the effectiveness of the monitoring system, the experiment is conducted through the corner milling of a titanium alloy workpiece. Five metrics, <i>PUR</i>, <i>CSM</i>, <i>NMI</i>, <i>CluCE</i> and <i>ClaCE</i>, are used to evaluate the effectiveness of the recognition results. Compared with the state-of-the-art supervised methods, our method provides commensurate monitoring effectiveness but requires much fewer test data to build the model, which greatly reduces the operating cost of the TCM system. |
| first_indexed | 2024-03-09T13:04:51Z |
| format | Article |
| id | doaj.art-841e0e7cb6594f0ab35a6f5b526a972f |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T13:04:51Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-841e0e7cb6594f0ab35a6f5b526a972f2023-11-30T21:50:26ZengMDPI AGMachines2075-17022022-07-0110861610.3390/machines10080616Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent MethodZhimeng Li0Wen Zhong1Yonggang Shi2Ming Yu3Jian Zhao4Guofeng Wang5School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300350, ChinaReal-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize tool conditions. On the other hand, it is costly to obtain sufficient test data on corner milling for most of the current pattern recognition methods, which are based on the supervised method. In this work, we propose a time-frequency intrinsic feature extraction strategy of acoustic emission signal (AEs) to construct a cutting condition-independent method for tool wear monitoring. The proposed new feature-extraction strategy is used to obtain the tool wear conditions through the intrinsic information of the time-frequency image of AEs. In addition, an unsupervised tool condition recognition framework, including the unsupervised feature selection, the clustering based on adjacent grids searching (CAGS) and the density factor based on CAGS, is proposed to determine the relationship between tool wear values and AE features. To test the effectiveness of the monitoring system, the experiment is conducted through the corner milling of a titanium alloy workpiece. Five metrics, <i>PUR</i>, <i>CSM</i>, <i>NMI</i>, <i>CluCE</i> and <i>ClaCE</i>, are used to evaluate the effectiveness of the recognition results. Compared with the state-of-the-art supervised methods, our method provides commensurate monitoring effectiveness but requires much fewer test data to build the model, which greatly reduces the operating cost of the TCM system.https://www.mdpi.com/2075-1702/10/8/616tool wear monitoringcorner-millingunsupervised |
| spellingShingle | Zhimeng Li Wen Zhong Yonggang Shi Ming Yu Jian Zhao Guofeng Wang Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method Machines tool wear monitoring corner-milling unsupervised |
| title | Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method |
| title_full | Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method |
| title_fullStr | Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method |
| title_full_unstemmed | Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method |
| title_short | Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method |
| title_sort | unsupervised tool wear monitoring in the corner milling of a titanium alloy based on a cutting condition independent method |
| topic | tool wear monitoring corner-milling unsupervised |
| url | https://www.mdpi.com/2075-1702/10/8/616 |
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