Prediction of Remaining Life of Cutting Tool Based on DNN
In order to better solve the problem that the remaining life of cutting tool is difficult to predict accurately, this paper studies three aspects of the selection of monitoring indexes, the extraction of data features and the establishing of prediction models Firstly, Cutting force and vibration fre...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2019-06-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1674 |
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| _version_ | 1849341060747100160 |
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| author | LIU Sheng-hui ZHANG Ren-jing ZHANG Shu-li MA Chao ZHANG Hong-guo |
| author_facet | LIU Sheng-hui ZHANG Ren-jing ZHANG Shu-li MA Chao ZHANG Hong-guo |
| author_sort | LIU Sheng-hui |
| collection | DOAJ |
| description | In order to better solve the problem that the remaining life of cutting tool is difficult to predict accurately, this paper studies three aspects of the selection of monitoring indexes, the extraction of data features and the establishing of prediction models Firstly, Cutting force and vibration frequency were selected as the indirect monitoring indexes of cutting tool These two indexes can accurately reflect the state of cutting tool, and also can solve the problem that the selecting the direct monitoring indexes causes, the wear analysis results of cutting tool being too subjective in the traditional state monitoring method Secondly, feature extraction is carried out by using wavelet packet analysis, and then the entropy values of the monitoring data are obtained They are taken as the input data Thirdly, the input data are used as the training data and testing data of the prediction model based on Deep Neural Network (DNN) Finally, the simulation experiments of the prediction method are carried out by using the real data of the workshop The results show that the model can effectively predict the useful life |
| format | Article |
| id | doaj-art-61673f62db7345ceb161e9a5ac84b68a |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2019-06-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-61673f62db7345ceb161e9a5ac84b68a2025-08-20T03:43:43ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832019-06-0124031810.15938/j.jhust.2019.03.001Prediction of Remaining Life of Cutting Tool Based on DNNLIU Sheng-hui0ZHANG Ren-jing1ZHANG Shu-li2MA Chao3ZHANG Hong-guo4School of Software, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Software, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Software, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Software, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Software, Harbin University of Science and Technology, Harbin 150080, ChinaIn order to better solve the problem that the remaining life of cutting tool is difficult to predict accurately, this paper studies three aspects of the selection of monitoring indexes, the extraction of data features and the establishing of prediction models Firstly, Cutting force and vibration frequency were selected as the indirect monitoring indexes of cutting tool These two indexes can accurately reflect the state of cutting tool, and also can solve the problem that the selecting the direct monitoring indexes causes, the wear analysis results of cutting tool being too subjective in the traditional state monitoring method Secondly, feature extraction is carried out by using wavelet packet analysis, and then the entropy values of the monitoring data are obtained They are taken as the input data Thirdly, the input data are used as the training data and testing data of the prediction model based on Deep Neural Network (DNN) Finally, the simulation experiments of the prediction method are carried out by using the real data of the workshop The results show that the model can effectively predict the useful lifehttps://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1674deep neural networkcutting forcefeature extractionprediction of remaining useful life |
| spellingShingle | LIU Sheng-hui ZHANG Ren-jing ZHANG Shu-li MA Chao ZHANG Hong-guo Prediction of Remaining Life of Cutting Tool Based on DNN Journal of Harbin University of Science and Technology deep neural network cutting force feature extraction prediction of remaining useful life |
| title | Prediction of Remaining Life of Cutting Tool Based on DNN |
| title_full | Prediction of Remaining Life of Cutting Tool Based on DNN |
| title_fullStr | Prediction of Remaining Life of Cutting Tool Based on DNN |
| title_full_unstemmed | Prediction of Remaining Life of Cutting Tool Based on DNN |
| title_short | Prediction of Remaining Life of Cutting Tool Based on DNN |
| title_sort | prediction of remaining life of cutting tool based on dnn |
| topic | deep neural network cutting force feature extraction prediction of remaining useful life |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1674 |
| work_keys_str_mv | AT liushenghui predictionofremaininglifeofcuttingtoolbasedondnn AT zhangrenjing predictionofremaininglifeofcuttingtoolbasedondnn AT zhangshuli predictionofremaininglifeofcuttingtoolbasedondnn AT machao predictionofremaininglifeofcuttingtoolbasedondnn AT zhanghongguo predictionofremaininglifeofcuttingtoolbasedondnn |