Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network
To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/11/6/809 |
| _version_ | 1798038467518660608 |
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| author | Yiting Li Qingsheng Xie Haisong Huang Qipeng Chen |
| author_facet | Yiting Li Qingsheng Xie Haisong Huang Qipeng Chen |
| author_sort | Yiting Li |
| collection | DOAJ |
| description | To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios. |
| first_indexed | 2024-04-11T21:40:36Z |
| format | Article |
| id | doaj.art-b56e264bfa5f4e8286d61cf6e410be9f |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T21:40:36Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-b56e264bfa5f4e8286d61cf6e410be9f2022-12-22T04:01:37ZengMDPI AGSymmetry2073-89942019-06-0111680910.3390/sym11060809sym11060809Research on a Tool Wear Monitoring Algorithm Based on Residual Dense NetworkYiting Li0Qingsheng Xie1Haisong Huang2Qipeng Chen3Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, ChinaTo accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.https://www.mdpi.com/2073-8994/11/6/809tool wearresidual dense networkwavelet denoisingconvolutional neural network |
| spellingShingle | Yiting Li Qingsheng Xie Haisong Huang Qipeng Chen Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network Symmetry tool wear residual dense network wavelet denoising convolutional neural network |
| title | Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network |
| title_full | Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network |
| title_fullStr | Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network |
| title_full_unstemmed | Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network |
| title_short | Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network |
| title_sort | research on a tool wear monitoring algorithm based on residual dense network |
| topic | tool wear residual dense network wavelet denoising convolutional neural network |
| url | https://www.mdpi.com/2073-8994/11/6/809 |
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