Tool health monitoring and prediction via attention-based encoder-decoder with a multi-step mechanism
In metal cutting processing, prediction of the tool wear or VB value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2022-09, Vol.122 (2), p.685-695 |
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| container_title | International journal of advanced manufacturing technology |
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| creator | Guo, Baosu Zhang, Qin Peng, Qinjing Zhuang, Jichao Wu, Fenghe Zhang, Quan |
| description | In metal cutting processing, prediction of the tool wear or
VB
value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its next time moment without considering the tool states at different time points. It ignores important information of the tool wear. In this paper, we propose a comprehensive model, which consists of a monitoring module and a prediction module, to monitor and predict the tool wear for the first time. In the monitoring module, a DenseNet model is constructed to monitor the tool wear via sensor signals. In addition, the prediction module based on attention mechanism is developed by simulating the human brain attention to selectively focus on the important part of processing sequence information. An encoder-decoder structure is introduced for a multi-step prediction of the tool wear. Several
VB
values in the nearest future are predicted by using sequential
VB
values monitored in the latest past. Experimental studies show that the short-term information has more influence on the tool wear prediction than the long-term information. The proposed method has been used to predict multi-step
VB
values in milling operations. |
| doi_str_mv | 10.1007/s00170-022-09894-7 |
| format | Article |
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VB
value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its next time moment without considering the tool states at different time points. It ignores important information of the tool wear. In this paper, we propose a comprehensive model, which consists of a monitoring module and a prediction module, to monitor and predict the tool wear for the first time. In the monitoring module, a DenseNet model is constructed to monitor the tool wear via sensor signals. In addition, the prediction module based on attention mechanism is developed by simulating the human brain attention to selectively focus on the important part of processing sequence information. An encoder-decoder structure is introduced for a multi-step prediction of the tool wear. Several
VB
values in the nearest future are predicted by using sequential
VB
values monitored in the latest past. Experimental studies show that the short-term information has more influence on the tool wear prediction than the long-term information. The proposed method has been used to predict multi-step
VB
values in milling operations.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-022-09894-7</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Accuracy ; Acoustics ; Advanced manufacturing technologies ; Artificial neural networks ; CAE) and Design ; Coders ; Computer-Aided Engineering (CAD ; Cutting wear ; Deep learning ; Encoders-Decoders ; Engineering ; Fault diagnosis ; Industrial and Production Engineering ; Machine learning ; Manufacturing ; Mechanical Engineering ; Media Management ; Metal cutting ; Methods ; Modules ; Neural networks ; Original Article ; Productivity ; Sensors ; Service life ; Time series ; Tool replacement ; Tool wear</subject><ispartof>International journal of advanced manufacturing technology, 2022-09, Vol.122 (2), p.685-695</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-7eef4d8671c6898f7501717e1482ee1130d4f73ea88baa2282c1c3e2c92d29b53</citedby><cites>FETCH-LOGICAL-c319t-7eef4d8671c6898f7501717e1482ee1130d4f73ea88baa2282c1c3e2c92d29b53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00170-022-09894-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00170-022-09894-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Guo, Baosu</creatorcontrib><creatorcontrib>Zhang, Qin</creatorcontrib><creatorcontrib>Peng, Qinjing</creatorcontrib><creatorcontrib>Zhuang, Jichao</creatorcontrib><creatorcontrib>Wu, Fenghe</creatorcontrib><creatorcontrib>Zhang, Quan</creatorcontrib><title>Tool health monitoring and prediction via attention-based encoder-decoder with a multi-step mechanism</title><title>International journal of advanced manufacturing technology</title><addtitle>Int J Adv Manuf Technol</addtitle><description>In metal cutting processing, prediction of the tool wear or
VB
value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its next time moment without considering the tool states at different time points. It ignores important information of the tool wear. In this paper, we propose a comprehensive model, which consists of a monitoring module and a prediction module, to monitor and predict the tool wear for the first time. In the monitoring module, a DenseNet model is constructed to monitor the tool wear via sensor signals. In addition, the prediction module based on attention mechanism is developed by simulating the human brain attention to selectively focus on the important part of processing sequence information. An encoder-decoder structure is introduced for a multi-step prediction of the tool wear. Several
VB
values in the nearest future are predicted by using sequential
VB
values monitored in the latest past. Experimental studies show that the short-term information has more influence on the tool wear prediction than the long-term information. The proposed method has been used to predict multi-step
VB
values in milling operations.</description><subject>Accuracy</subject><subject>Acoustics</subject><subject>Advanced manufacturing technologies</subject><subject>Artificial neural networks</subject><subject>CAE) and Design</subject><subject>Coders</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Cutting wear</subject><subject>Deep learning</subject><subject>Encoders-Decoders</subject><subject>Engineering</subject><subject>Fault diagnosis</subject><subject>Industrial and Production Engineering</subject><subject>Machine learning</subject><subject>Manufacturing</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Metal cutting</subject><subject>Methods</subject><subject>Modules</subject><subject>Neural networks</subject><subject>Original Article</subject><subject>Productivity</subject><subject>Sensors</subject><subject>Service life</subject><subject>Time series</subject><subject>Tool replacement</subject><subject>Tool wear</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kEtLAzEUhYMoWKt_wFXAdTSP6SSzlOILCm7qOqTJHZsyk4xJqvjvnXYEd64OB845l_shdM3oLaNU3mVKmaSEck5oo5qKyBM0Y5UQRFC2OEUzymtFhKzVObrIeTfGa1arGYJ1jB3egunKFvcx-BKTD-_YBIeHBM7b4mPAn95gUwqEgyMbk8FhCDY6SMTBUfGXHycM7vdd8SQXGHAPdmuCz_0lOmtNl-HqV-fo7fFhvXwmq9enl-X9iljBmkIkQFs5VUtma9WoVi7Gr5gEVikOwJigrmqlAKPUxhjOFbfMCuC24Y43m4WYo5tpd0jxYw-56F3cpzCe1FwyVgsuKjWm-JSyKeacoNVD8r1J35pRfcCpJ5x6xKmPOLUcS2Iq5eEACNLf9D-tH8MWeOA</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>Guo, Baosu</creator><creator>Zhang, Qin</creator><creator>Peng, Qinjing</creator><creator>Zhuang, Jichao</creator><creator>Wu, Fenghe</creator><creator>Zhang, Quan</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20220901</creationdate><title>Tool health monitoring and prediction via attention-based encoder-decoder with a multi-step mechanism</title><author>Guo, Baosu ; Zhang, Qin ; Peng, Qinjing ; Zhuang, Jichao ; Wu, Fenghe ; Zhang, Quan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-7eef4d8671c6898f7501717e1482ee1130d4f73ea88baa2282c1c3e2c92d29b53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Acoustics</topic><topic>Advanced manufacturing technologies</topic><topic>Artificial neural networks</topic><topic>CAE) and Design</topic><topic>Coders</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Cutting wear</topic><topic>Deep learning</topic><topic>Encoders-Decoders</topic><topic>Engineering</topic><topic>Fault diagnosis</topic><topic>Industrial and Production Engineering</topic><topic>Machine learning</topic><topic>Manufacturing</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Metal cutting</topic><topic>Methods</topic><topic>Modules</topic><topic>Neural networks</topic><topic>Original Article</topic><topic>Productivity</topic><topic>Sensors</topic><topic>Service life</topic><topic>Time series</topic><topic>Tool replacement</topic><topic>Tool wear</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Baosu</creatorcontrib><creatorcontrib>Zhang, Qin</creatorcontrib><creatorcontrib>Peng, Qinjing</creatorcontrib><creatorcontrib>Zhuang, Jichao</creatorcontrib><creatorcontrib>Wu, Fenghe</creatorcontrib><creatorcontrib>Zhang, Quan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Baosu</au><au>Zhang, Qin</au><au>Peng, Qinjing</au><au>Zhuang, Jichao</au><au>Wu, Fenghe</au><au>Zhang, Quan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tool health monitoring and prediction via attention-based encoder-decoder with a multi-step mechanism</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2022-09-01</date><risdate>2022</risdate><volume>122</volume><issue>2</issue><spage>685</spage><epage>695</epage><pages>685-695</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>In metal cutting processing, prediction of the tool wear or
VB
value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its next time moment without considering the tool states at different time points. It ignores important information of the tool wear. In this paper, we propose a comprehensive model, which consists of a monitoring module and a prediction module, to monitor and predict the tool wear for the first time. In the monitoring module, a DenseNet model is constructed to monitor the tool wear via sensor signals. In addition, the prediction module based on attention mechanism is developed by simulating the human brain attention to selectively focus on the important part of processing sequence information. An encoder-decoder structure is introduced for a multi-step prediction of the tool wear. Several
VB
values in the nearest future are predicted by using sequential
VB
values monitored in the latest past. Experimental studies show that the short-term information has more influence on the tool wear prediction than the long-term information. The proposed method has been used to predict multi-step
VB
values in milling operations.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-022-09894-7</doi><tpages>11</tpages></addata></record> |
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| subjects | Accuracy Acoustics Advanced manufacturing technologies Artificial neural networks CAE) and Design Coders Computer-Aided Engineering (CAD Cutting wear Deep learning Encoders-Decoders Engineering Fault diagnosis Industrial and Production Engineering Machine learning Manufacturing Mechanical Engineering Media Management Metal cutting Methods Modules Neural networks Original Article Productivity Sensors Service life Time series Tool replacement Tool wear |
| title | Tool health monitoring and prediction via attention-based encoder-decoder with a multi-step mechanism |
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