A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear
Diamond cutting-tool wear has a direct impact on the processing accuracy of the machined surface in ultra-precision diamond cutting. It is difficult to monitor the tool’s condition because of the slight wear amount. This paper proposed a hybrid deep learning model for tool wear state prediction in u...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/11/6675 |
| _version_ | 1827739737792708608 |
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| author | Lei Wu Kaijie Sha Ye Tao Bingfeng Ju Yuanliu Chen |
| author_facet | Lei Wu Kaijie Sha Ye Tao Bingfeng Ju Yuanliu Chen |
| author_sort | Lei Wu |
| collection | DOAJ |
| description | Diamond cutting-tool wear has a direct impact on the processing accuracy of the machined surface in ultra-precision diamond cutting. It is difficult to monitor the tool’s condition because of the slight wear amount. This paper proposed a hybrid deep learning model for tool wear state prediction in ultra-precision diamond cutting. The cutting force was accurately estimated and the wear state of the diamond tool was predicted by using the hybrid deep learning model with the motion displacement, velocity, and other signals in the machining process. By carrying out machining experiments, this method can classify diamond-tool wear condition with an accuracy of more than 85%. Meanwhile, the effectiveness of the proposed method was verified by comparing it with a variety of machine learning models. |
| first_indexed | 2024-03-11T03:11:22Z |
| format | Article |
| id | doaj.art-4759a9388c144dda8e6fe94a69264c8a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:11:22Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4759a9388c144dda8e6fe94a69264c8a2023-11-18T07:35:13ZengMDPI AGApplied Sciences2076-34172023-05-011311667510.3390/app13116675A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool WearLei Wu0Kaijie Sha1Ye Tao2Bingfeng Ju3Yuanliu Chen4The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaDiamond cutting-tool wear has a direct impact on the processing accuracy of the machined surface in ultra-precision diamond cutting. It is difficult to monitor the tool’s condition because of the slight wear amount. This paper proposed a hybrid deep learning model for tool wear state prediction in ultra-precision diamond cutting. The cutting force was accurately estimated and the wear state of the diamond tool was predicted by using the hybrid deep learning model with the motion displacement, velocity, and other signals in the machining process. By carrying out machining experiments, this method can classify diamond-tool wear condition with an accuracy of more than 85%. Meanwhile, the effectiveness of the proposed method was verified by comparing it with a variety of machine learning models.https://www.mdpi.com/2076-3417/13/11/6675ultra-precision diamond cuttingcutting-tool wearin-process predictiondigital twindeep learning |
| spellingShingle | Lei Wu Kaijie Sha Ye Tao Bingfeng Ju Yuanliu Chen A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear Applied Sciences ultra-precision diamond cutting cutting-tool wear in-process prediction digital twin deep learning |
| title | A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear |
| title_full | A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear |
| title_fullStr | A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear |
| title_full_unstemmed | A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear |
| title_short | A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear |
| title_sort | hybrid deep learning model as the digital twin of ultra precision diamond cutting for in process prediction of cutting tool wear |
| topic | ultra-precision diamond cutting cutting-tool wear in-process prediction digital twin deep learning |
| url | https://www.mdpi.com/2076-3417/13/11/6675 |
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