Prediction of Abrasive Belt Wear Based on BP Neural Network
Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/9/12/314 |
| _version_ | 1797502882127282176 |
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| author | Yuanxun Cao Ji Zhao Xingtian Qu Xin Wang Bowen Liu |
| author_facet | Yuanxun Cao Ji Zhao Xingtian Qu Xin Wang Bowen Liu |
| author_sort | Yuanxun Cao |
| collection | DOAJ |
| description | Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency. |
| first_indexed | 2024-03-10T03:42:30Z |
| format | Article |
| id | doaj.art-ebccbf1dde284c8dae461fc004009872 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T03:42:30Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-ebccbf1dde284c8dae461fc0040098722023-11-23T09:16:25ZengMDPI AGMachines2075-17022021-11-0191231410.3390/machines9120314Prediction of Abrasive Belt Wear Based on BP Neural NetworkYuanxun Cao0Ji Zhao1Xingtian Qu2Xin Wang3Bowen Liu4School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, ChinaSchool of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, ChinaSchool of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, ChinaSchool of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, ChinaSchool of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, ChinaAbrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency.https://www.mdpi.com/2075-1702/9/12/314abrasive beltweargrindingpredictionBP neural network |
| spellingShingle | Yuanxun Cao Ji Zhao Xingtian Qu Xin Wang Bowen Liu Prediction of Abrasive Belt Wear Based on BP Neural Network Machines abrasive belt wear grinding prediction BP neural network |
| title | Prediction of Abrasive Belt Wear Based on BP Neural Network |
| title_full | Prediction of Abrasive Belt Wear Based on BP Neural Network |
| title_fullStr | Prediction of Abrasive Belt Wear Based on BP Neural Network |
| title_full_unstemmed | Prediction of Abrasive Belt Wear Based on BP Neural Network |
| title_short | Prediction of Abrasive Belt Wear Based on BP Neural Network |
| title_sort | prediction of abrasive belt wear based on bp neural network |
| topic | abrasive belt wear grinding prediction BP neural network |
| url | https://www.mdpi.com/2075-1702/9/12/314 |
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