A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM
Machine learning models have been widely used in the field of cutting tool wear identification, achieving favorable results. However, in actual industrial scenarios, obtaining sufficient labeled samples is time consuming and costly, while unlabeled samples are abundant and easy to collect. This situ...
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MDPI AG
2025-02-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/13/2/72 |
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| author | Xin He Meipeng Zhong Chengcheng He Jinhao Wu Haiyang Yang Zhigao Zhao Wei Yang Cong Jing Yanlin Li Chen Gao |
| author_facet | Xin He Meipeng Zhong Chengcheng He Jinhao Wu Haiyang Yang Zhigao Zhao Wei Yang Cong Jing Yanlin Li Chen Gao |
| author_sort | Xin He |
| collection | DOAJ |
| description | Machine learning models have been widely used in the field of cutting tool wear identification, achieving favorable results. However, in actual industrial scenarios, obtaining sufficient labeled samples is time consuming and costly, while unlabeled samples are abundant and easy to collect. This situation significantly affects the model’s performance. To address this challenge, a novel semi-supervised method, based on long short-term memory (LSTM) networks, is provided. The proposed method leverages both small labeled and abundant unlabeled data to improve tool wear identification performance. The proposed method trains an initial tool wear regression model using LSTM, using a small amount of labeled samples. It then uses manifold regularization to generate pseudo-labels for the unlabeled samples. These pseudo-labeled samples are combined with the original labeled samples to retrain the MR–LSTM model iteratively to improve its performance. This process continues until a termination condition is met. The method considers the correlation between sample labels and feature structures, as well as the correlation between global and local sample labels. Experiments involving milling tool wear identification demonstrate that the proposed method significantly outperforms support vector regression (SVR) and recurrent neural network (RNN)-based methods, when a small amount of labeled samples and abundant unlabeled samples are available. The average <i>R<sup>2</sup></i> values in terms of the proposed method’s predicted results can reach above 0.95. The proposed method is a potential technique for low-cost tool wear identification, without the need to collect a large number of labeled samples. |
| format | Article |
| id | doaj-art-c94de329148d4c728fb7b27579cd8d8b |
| institution | DOAJ |
| issn | 2075-4442 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj-art-c94de329148d4c728fb7b27579cd8d8b2025-08-20T02:44:39ZengMDPI AGLubricants2075-44422025-02-011327210.3390/lubricants13020072A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTMXin He0Meipeng Zhong1Chengcheng He2Jinhao Wu3Haiyang Yang4Zhigao Zhao5Wei Yang6Cong Jing7Yanlin Li8Chen Gao9College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaCollege of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaZhejiang XCC Group Co., Ltd., Shaoxing 312500, ChinaCollege of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaCollege of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaZhejiang XCC Group Co., Ltd., Shaoxing 312500, ChinaZhejiang XCC Group Co., Ltd., Shaoxing 312500, ChinaCollege of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaCollege of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing 314003, ChinaMachine learning models have been widely used in the field of cutting tool wear identification, achieving favorable results. However, in actual industrial scenarios, obtaining sufficient labeled samples is time consuming and costly, while unlabeled samples are abundant and easy to collect. This situation significantly affects the model’s performance. To address this challenge, a novel semi-supervised method, based on long short-term memory (LSTM) networks, is provided. The proposed method leverages both small labeled and abundant unlabeled data to improve tool wear identification performance. The proposed method trains an initial tool wear regression model using LSTM, using a small amount of labeled samples. It then uses manifold regularization to generate pseudo-labels for the unlabeled samples. These pseudo-labeled samples are combined with the original labeled samples to retrain the MR–LSTM model iteratively to improve its performance. This process continues until a termination condition is met. The method considers the correlation between sample labels and feature structures, as well as the correlation between global and local sample labels. Experiments involving milling tool wear identification demonstrate that the proposed method significantly outperforms support vector regression (SVR) and recurrent neural network (RNN)-based methods, when a small amount of labeled samples and abundant unlabeled samples are available. The average <i>R<sup>2</sup></i> values in terms of the proposed method’s predicted results can reach above 0.95. The proposed method is a potential technique for low-cost tool wear identification, without the need to collect a large number of labeled samples.https://www.mdpi.com/2075-4442/13/2/72tool wear identificationlong short-term memorymanifold regularizationsemi-supervised learning |
| spellingShingle | Xin He Meipeng Zhong Chengcheng He Jinhao Wu Haiyang Yang Zhigao Zhao Wei Yang Cong Jing Yanlin Li Chen Gao A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM Lubricants tool wear identification long short-term memory manifold regularization semi-supervised learning |
| title | A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM |
| title_full | A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM |
| title_fullStr | A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM |
| title_full_unstemmed | A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM |
| title_short | A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM |
| title_sort | novel tool wear identification method based on a semi supervised lstm |
| topic | tool wear identification long short-term memory manifold regularization semi-supervised learning |
| url | https://www.mdpi.com/2075-4442/13/2/72 |
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