Research on On-line Tool Wear Monitoring Technology Based on GPR
At present, many kinds of sensors are used for on-line monitoring of cutting process, tool identification and timely replacement. However, most of the original monitoring signals extracted from the cutting process are time series signals, which contain too much process noise. As the signal noise is...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/28/e3sconf_pgsge2021_01046.pdf |
| _version_ | 1818645977085509632 |
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| author | Fan Shan Huang Yi Zeng Haixia |
| author_facet | Fan Shan Huang Yi Zeng Haixia |
| author_sort | Fan Shan |
| collection | DOAJ |
| description | At present, many kinds of sensors are used for on-line monitoring of cutting process, tool identification and timely replacement. However, most of the original monitoring signals extracted from the cutting process are time series signals, which contain too much process noise. As the signal noise is relatively low, it is difficult to establish a direct relationship with the tool wear. Therefore, how to obtain the effective information from the online monitoring signal and extract the characteristics that can directly reflect the tool wear from the complex original signal, so as to establish an effective and reliable tool wear monitoring system, is the key and difficult problem in the research of the online monitoring technology of tool wear. Firstly, an experimental platform based on the force sensor for on-line monitoring of tool wear was built, and the signal obtained by the force sensor was used to monitor the tool wear, and the feature information was extracted and fused. The innovation of the project lies in the use of Gaussian process regression (GPR) method to predict the tool wear, the use of feature dimensional rise technology, to reduce the impact of noise, on the premise of ensuring the prediction accuracy, improve the confidence interval of GPR prediction results, improve the stability and reliability of the monitoring process. |
| first_indexed | 2024-12-17T00:39:18Z |
| format | Article |
| id | doaj.art-7ada42cb236947689571ef9d7cf3a909 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-17T00:39:18Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-7ada42cb236947689571ef9d7cf3a9092022-12-21T22:10:03ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012520104610.1051/e3sconf/202125201046e3sconf_pgsge2021_01046Research on On-line Tool Wear Monitoring Technology Based on GPRFan Shan0Huang Yi1Zeng Haixia2Mechanical & Electronic Engineering Division, WenHua CollegeMechanical & Electronic Engineering Division, WenHua CollegeMechanical & Electronic Engineering Division, WenHua CollegeAt present, many kinds of sensors are used for on-line monitoring of cutting process, tool identification and timely replacement. However, most of the original monitoring signals extracted from the cutting process are time series signals, which contain too much process noise. As the signal noise is relatively low, it is difficult to establish a direct relationship with the tool wear. Therefore, how to obtain the effective information from the online monitoring signal and extract the characteristics that can directly reflect the tool wear from the complex original signal, so as to establish an effective and reliable tool wear monitoring system, is the key and difficult problem in the research of the online monitoring technology of tool wear. Firstly, an experimental platform based on the force sensor for on-line monitoring of tool wear was built, and the signal obtained by the force sensor was used to monitor the tool wear, and the feature information was extracted and fused. The innovation of the project lies in the use of Gaussian process regression (GPR) method to predict the tool wear, the use of feature dimensional rise technology, to reduce the impact of noise, on the premise of ensuring the prediction accuracy, improve the confidence interval of GPR prediction results, improve the stability and reliability of the monitoring process.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/28/e3sconf_pgsge2021_01046.pdf |
| spellingShingle | Fan Shan Huang Yi Zeng Haixia Research on On-line Tool Wear Monitoring Technology Based on GPR E3S Web of Conferences |
| title | Research on On-line Tool Wear Monitoring Technology Based on GPR |
| title_full | Research on On-line Tool Wear Monitoring Technology Based on GPR |
| title_fullStr | Research on On-line Tool Wear Monitoring Technology Based on GPR |
| title_full_unstemmed | Research on On-line Tool Wear Monitoring Technology Based on GPR |
| title_short | Research on On-line Tool Wear Monitoring Technology Based on GPR |
| title_sort | research on on line tool wear monitoring technology based on gpr |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/28/e3sconf_pgsge2021_01046.pdf |
| work_keys_str_mv | AT fanshan researchononlinetoolwearmonitoringtechnologybasedongpr AT huangyi researchononlinetoolwearmonitoringtechnologybasedongpr AT zenghaixia researchononlinetoolwearmonitoringtechnologybasedongpr |