Tool Wear Condition Monitoring Based on Improved Symmetrized Dot Pattern Enhanced Resnet18 Under Small Samples
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| 发表在: | Lubricants vol. 13, no. 11 (2025), p. 503-517 |
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| 其他作者: | , , , |
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MDPI AG
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| 在线阅读: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3275541642 | ||
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| 022 | |a 2075-4442 | ||
| 024 | 7 | |a 10.3390/lubricants13110503 |2 doi | |
| 035 | |a 3275541642 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231530 |2 nlm | ||
| 100 | 1 | |a Chen, Xiaoqin |u School of Business Management, Jiaxing Nanhu University, Jiaxing 314001, China; annachen230014@jxnhu.edu.cn | |
| 245 | 1 | |a Tool Wear Condition Monitoring Based on Improved Symmetrized Dot Pattern Enhanced Resnet18 Under Small Samples | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Timely and effective identification of the tool wear condition is crucial for ensuring the machining quality of CNC machine tools. In most industrial scenarios, the cost of sample collection is high, so only a small number of samples are available for model training, making it difficult for the existing tool wear condition monitoring (TCM) methods based on deep learning to achieve high performance. To address this problem, this paper proposes a TCM method based on the improved symmetric dot pattern (SDP) enhanced ResNet18. Firstly, the time series sample data is converted into grayscale matrices through SDP, the correlation coefficient between the grayscale matrices is calculated, and the optimal parameter combination of SDP is determined according to the objective of minimizing the correlation coefficient. Then, the cutting force signal is converted into a lobe diagram of the optimized SDP to enrich the sample feature information. Next, the SDP lobe diagram is input into ResNet18 for few-shot learning. The results of a series of TCM experiments demonstrate that the proposed method is significantly superior to the STFT and GAF based methods. | |
| 653 | |a Cutting force | ||
| 653 | |a Machining | ||
| 653 | |a Signal processing | ||
| 653 | |a Symmetrized dot pattern | ||
| 653 | |a Symmetry | ||
| 653 | |a Gray scale | ||
| 653 | |a Machine tools | ||
| 653 | |a Cutting tools | ||
| 653 | |a Methods | ||
| 653 | |a Deep learning | ||
| 653 | |a Time series | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Correlation coefficients | ||
| 653 | |a Tool wear | ||
| 653 | |a Condition monitoring | ||
| 700 | 1 | |a Wang Gonghai |u Jiaxing Key Laboratory of Intelligent Manufacturing and Operation & Maintenance of Automotive Parts, Jiaxing Nanhu University, Jiaxing 314001, China; 22451439008@stu.wzu.edu.cn | |
| 700 | 1 | |a Fu Yuandie |u Jiaxing Key Laboratory of Intelligent Manufacturing and Operation & Maintenance of Automotive Parts, Jiaxing Nanhu University, Jiaxing 314001, China; 22451439008@stu.wzu.edu.cn | |
| 700 | 1 | |a Zhang, Huan |u School of Photovoltaic Modern Industry, Jiaxing Nanhu University, Jiaxing 314001, China; ayahuan1018@163.com | |
| 700 | 1 | |a Chen, Gao |u School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing 314003, China | |
| 773 | 0 | |t Lubricants |g vol. 13, no. 11 (2025), p. 503-517 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275541642/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275541642/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275541642/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |