Tool wear monitoring method based on AMIDBOAB and imbalanced data optimization
Development of precise models for monitoring tool wear faces challenges due to imbalance of experimental data. To address the issues of data imbalance and low monitoring accuracy in various tool wear stages of CNC machine tools, an AMIDBOAB tool wear monitoring method based on imbalanced data optimi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-06-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0272254 |
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| _version_ | 1849422414503477248 |
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| author | Guan-Hua Xu Hong-Yu Wu Bo Tang Jun-Long Xu |
| author_facet | Guan-Hua Xu Hong-Yu Wu Bo Tang Jun-Long Xu |
| author_sort | Guan-Hua Xu |
| collection | DOAJ |
| description | Development of precise models for monitoring tool wear faces challenges due to imbalance of experimental data. To address the issues of data imbalance and low monitoring accuracy in various tool wear stages of CNC machine tools, an AMIDBOAB tool wear monitoring method based on imbalanced data optimization is proposed. The multi-scale and multi-domain value feature extraction method, the maximum relevance minimum redundancy feature selection method, and the adaptive gravitation mixing sampling algorithm are integrated to optimize the dataset. Introducing circle chaotic mapping, Levy flight strategy, and adaptive variable inertia weight, a multiple improvement dung beetle optimizer algorithm is proposed to optimize the parameters of the base classifier in the adaptive boosting model. Experimental validation is carried out on the PHM2010 public dataset and a self-built ceramic machining dataset. The results indicate that the method significantly enhances the performance of the classification algorithm, achieving a tool wear monitoring accuracy of 95.06%. |
| format | Article |
| id | doaj-art-2e92dc3081dc44fe9322cf76b53d0ea3 |
| institution | Kabale University |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-2e92dc3081dc44fe9322cf76b53d0ea32025-08-20T03:31:06ZengAIP Publishing LLCAIP Advances2158-32262025-06-01156065209065209-1710.1063/5.0272254Tool wear monitoring method based on AMIDBOAB and imbalanced data optimizationGuan-Hua Xu0Hong-Yu Wu1Bo Tang2Jun-Long Xu3State Key Laboratory of Fluid Power and Mechatronic Systems, Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, Zhejiang University, Hangzhou 310027, ChinaCollege of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, ChinaCollege of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, ChinaCGNPC Inspection Technology Co., Ltd., Suzhou, ChinaDevelopment of precise models for monitoring tool wear faces challenges due to imbalance of experimental data. To address the issues of data imbalance and low monitoring accuracy in various tool wear stages of CNC machine tools, an AMIDBOAB tool wear monitoring method based on imbalanced data optimization is proposed. The multi-scale and multi-domain value feature extraction method, the maximum relevance minimum redundancy feature selection method, and the adaptive gravitation mixing sampling algorithm are integrated to optimize the dataset. Introducing circle chaotic mapping, Levy flight strategy, and adaptive variable inertia weight, a multiple improvement dung beetle optimizer algorithm is proposed to optimize the parameters of the base classifier in the adaptive boosting model. Experimental validation is carried out on the PHM2010 public dataset and a self-built ceramic machining dataset. The results indicate that the method significantly enhances the performance of the classification algorithm, achieving a tool wear monitoring accuracy of 95.06%.http://dx.doi.org/10.1063/5.0272254 |
| spellingShingle | Guan-Hua Xu Hong-Yu Wu Bo Tang Jun-Long Xu Tool wear monitoring method based on AMIDBOAB and imbalanced data optimization AIP Advances |
| title | Tool wear monitoring method based on AMIDBOAB and imbalanced data optimization |
| title_full | Tool wear monitoring method based on AMIDBOAB and imbalanced data optimization |
| title_fullStr | Tool wear monitoring method based on AMIDBOAB and imbalanced data optimization |
| title_full_unstemmed | Tool wear monitoring method based on AMIDBOAB and imbalanced data optimization |
| title_short | Tool wear monitoring method based on AMIDBOAB and imbalanced data optimization |
| title_sort | tool wear monitoring method based on amidboab and imbalanced data optimization |
| url | http://dx.doi.org/10.1063/5.0272254 |
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