Thermal error modeling and compensation of spindle based on gate recurrent unit network
In view of the serious hysteresis and nonlinear relationship between the thermal error of CNC machine tool spindle and the temperature rise of spindle measuring points, a spindle thermal error prediction model combining sparrow search algorithm (SSA) and gate recurrent unit (GRU) is proposed. Taking...
Gespeichert in:
| Veröffentlicht in: | International journal of advanced manufacturing technology 2023-10, Vol.128 (11-12), p.5519-5528 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5528 |
|---|---|
| container_issue | 11-12 |
| container_start_page | 5519 |
| container_title | International journal of advanced manufacturing technology |
| container_volume | 128 |
| creator | Li, Yang Bai, Yinming Hou, Zhaoyang Nie, Zhe Zhang, Huijie |
| description | In view of the serious hysteresis and nonlinear relationship between the thermal error of CNC machine tool spindle and the temperature rise of spindle measuring points, a spindle thermal error prediction model combining sparrow search algorithm (SSA) and gate recurrent unit (GRU) is proposed. Taking the spindle of a precision machine tool as the research object, the thermal error and the temperature field of the spindle in idling state are measured. Select the temperature of the measuring point of the spindle as the input and the thermal error in Z-direction as the output, the thermal error prediction model is established by using GRU network. SSA is used to optimize the training parameters of GRU network, and finally a prediction model of SSA-GRU spindle Z-direction thermal error considering the influence of natural environment is established. The performance of the established model is verified by taking the test data of variable speed working condition as the robustness test set. The results show that SSA-GRU can be used for thermal error modeling and compensation, and the Z-direction thermal error of the machine tool spindle can be controlled within 8 μm, which has better prediction accuracy than the traditional network model. |
| doi_str_mv | 10.1007/s00170-023-12276-2 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2870824222</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2870824222</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-b8f48ab53a3f6fee1b1fa0dfc0b2acd1f3730a4bdc25f22aa1decfdc8c949d983</originalsourceid><addsrcrecordid>eNp9kEtLxDAUhYMoOI7-AVcB19E8Om26lMEXDLgZcRnS5Gbs2CY1SRH_vR0ruHN14fCdc-FD6JLRa0ZpdZMoZRUllAvCOK9Kwo_QghVCEEHZ6hgtKC8lEVUpT9FZSvsJL1kpF-h1-wax1x2GGEPEfbDQtX6HtbfYhH4An3Rug8fB4TS03naAG53A4inb6Qw4ghljBJ_x6NuMPeTPEN_P0YnTXYKL37tEL_d32_Uj2Tw_PK1vN8QIVmfSSFdI3ayEFq50AKxhTlPrDG24NpY5UQmqi8YavnKca80sGGeNNHVR21qKJbqad4cYPkZIWe3DGP30UnFZUckLzvlE8ZkyMaQUwakhtr2OX4pRdRCoZoFqEqh-BKpDScylNMF-B_Fv-p_WN3ukdbY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2870824222</pqid></control><display><type>article</type><title>Thermal error modeling and compensation of spindle based on gate recurrent unit network</title><source>Springer LINK 全文期刊数据库</source><creator>Li, Yang ; Bai, Yinming ; Hou, Zhaoyang ; Nie, Zhe ; Zhang, Huijie</creator><creatorcontrib>Li, Yang ; Bai, Yinming ; Hou, Zhaoyang ; Nie, Zhe ; Zhang, Huijie</creatorcontrib><description>In view of the serious hysteresis and nonlinear relationship between the thermal error of CNC machine tool spindle and the temperature rise of spindle measuring points, a spindle thermal error prediction model combining sparrow search algorithm (SSA) and gate recurrent unit (GRU) is proposed. Taking the spindle of a precision machine tool as the research object, the thermal error and the temperature field of the spindle in idling state are measured. Select the temperature of the measuring point of the spindle as the input and the thermal error in Z-direction as the output, the thermal error prediction model is established by using GRU network. SSA is used to optimize the training parameters of GRU network, and finally a prediction model of SSA-GRU spindle Z-direction thermal error considering the influence of natural environment is established. The performance of the established model is verified by taking the test data of variable speed working condition as the robustness test set. The results show that SSA-GRU can be used for thermal error modeling and compensation, and the Z-direction thermal error of the machine tool spindle can be controlled within 8 μm, which has better prediction accuracy than the traditional network model.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-023-12276-2</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Accuracy ; Advanced manufacturing technologies ; CAE) and Design ; Compensation ; Computer-Aided Engineering (CAD ; Cooling ; Deformation ; Engineering ; Error analysis ; Genetic algorithms ; Heat ; Industrial and Production Engineering ; Machine tools ; Manufacturing ; Mechanical Engineering ; Media Management ; Modelling ; Neural networks ; Original Article ; Prediction models ; Search algorithms ; Spindles ; Temperature distribution ; Time series</subject><ispartof>International journal of advanced manufacturing technology, 2023-10, Vol.128 (11-12), p.5519-5528</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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-b8f48ab53a3f6fee1b1fa0dfc0b2acd1f3730a4bdc25f22aa1decfdc8c949d983</citedby><cites>FETCH-LOGICAL-c319t-b8f48ab53a3f6fee1b1fa0dfc0b2acd1f3730a4bdc25f22aa1decfdc8c949d983</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/s00170-023-12276-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00170-023-12276-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27915,27916,41479,42548,51310</link.rule.ids></links><search><creatorcontrib>Li, Yang</creatorcontrib><creatorcontrib>Bai, Yinming</creatorcontrib><creatorcontrib>Hou, Zhaoyang</creatorcontrib><creatorcontrib>Nie, Zhe</creatorcontrib><creatorcontrib>Zhang, Huijie</creatorcontrib><title>Thermal error modeling and compensation of spindle based on gate recurrent unit network</title><title>International journal of advanced manufacturing technology</title><addtitle>Int J Adv Manuf Technol</addtitle><description>In view of the serious hysteresis and nonlinear relationship between the thermal error of CNC machine tool spindle and the temperature rise of spindle measuring points, a spindle thermal error prediction model combining sparrow search algorithm (SSA) and gate recurrent unit (GRU) is proposed. Taking the spindle of a precision machine tool as the research object, the thermal error and the temperature field of the spindle in idling state are measured. Select the temperature of the measuring point of the spindle as the input and the thermal error in Z-direction as the output, the thermal error prediction model is established by using GRU network. SSA is used to optimize the training parameters of GRU network, and finally a prediction model of SSA-GRU spindle Z-direction thermal error considering the influence of natural environment is established. The performance of the established model is verified by taking the test data of variable speed working condition as the robustness test set. The results show that SSA-GRU can be used for thermal error modeling and compensation, and the Z-direction thermal error of the machine tool spindle can be controlled within 8 μm, which has better prediction accuracy than the traditional network model.</description><subject>Accuracy</subject><subject>Advanced manufacturing technologies</subject><subject>CAE) and Design</subject><subject>Compensation</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Cooling</subject><subject>Deformation</subject><subject>Engineering</subject><subject>Error analysis</subject><subject>Genetic algorithms</subject><subject>Heat</subject><subject>Industrial and Production Engineering</subject><subject>Machine tools</subject><subject>Manufacturing</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Modelling</subject><subject>Neural networks</subject><subject>Original Article</subject><subject>Prediction models</subject><subject>Search algorithms</subject><subject>Spindles</subject><subject>Temperature distribution</subject><subject>Time series</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kEtLxDAUhYMoOI7-AVcB19E8Om26lMEXDLgZcRnS5Gbs2CY1SRH_vR0ruHN14fCdc-FD6JLRa0ZpdZMoZRUllAvCOK9Kwo_QghVCEEHZ6hgtKC8lEVUpT9FZSvsJL1kpF-h1-wax1x2GGEPEfbDQtX6HtbfYhH4An3Rug8fB4TS03naAG53A4inb6Qw4ghljBJ_x6NuMPeTPEN_P0YnTXYKL37tEL_d32_Uj2Tw_PK1vN8QIVmfSSFdI3ayEFq50AKxhTlPrDG24NpY5UQmqi8YavnKca80sGGeNNHVR21qKJbqad4cYPkZIWe3DGP30UnFZUckLzvlE8ZkyMaQUwakhtr2OX4pRdRCoZoFqEqh-BKpDScylNMF-B_Fv-p_WN3ukdbY</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Li, Yang</creator><creator>Bai, Yinming</creator><creator>Hou, Zhaoyang</creator><creator>Nie, Zhe</creator><creator>Zhang, Huijie</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>PTHSS</scope></search><sort><creationdate>20231001</creationdate><title>Thermal error modeling and compensation of spindle based on gate recurrent unit network</title><author>Li, Yang ; Bai, Yinming ; Hou, Zhaoyang ; Nie, Zhe ; Zhang, Huijie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-b8f48ab53a3f6fee1b1fa0dfc0b2acd1f3730a4bdc25f22aa1decfdc8c949d983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Advanced manufacturing technologies</topic><topic>CAE) and Design</topic><topic>Compensation</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Cooling</topic><topic>Deformation</topic><topic>Engineering</topic><topic>Error analysis</topic><topic>Genetic algorithms</topic><topic>Heat</topic><topic>Industrial and Production Engineering</topic><topic>Machine tools</topic><topic>Manufacturing</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Modelling</topic><topic>Neural networks</topic><topic>Original Article</topic><topic>Prediction models</topic><topic>Search algorithms</topic><topic>Spindles</topic><topic>Temperature distribution</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Yang</creatorcontrib><creatorcontrib>Bai, Yinming</creatorcontrib><creatorcontrib>Hou, Zhaoyang</creatorcontrib><creatorcontrib>Nie, Zhe</creatorcontrib><creatorcontrib>Zhang, Huijie</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>Engineering Collection</collection><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Yang</au><au>Bai, Yinming</au><au>Hou, Zhaoyang</au><au>Nie, Zhe</au><au>Zhang, Huijie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Thermal error modeling and compensation of spindle based on gate recurrent unit network</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2023-10-01</date><risdate>2023</risdate><volume>128</volume><issue>11-12</issue><spage>5519</spage><epage>5528</epage><pages>5519-5528</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>In view of the serious hysteresis and nonlinear relationship between the thermal error of CNC machine tool spindle and the temperature rise of spindle measuring points, a spindle thermal error prediction model combining sparrow search algorithm (SSA) and gate recurrent unit (GRU) is proposed. Taking the spindle of a precision machine tool as the research object, the thermal error and the temperature field of the spindle in idling state are measured. Select the temperature of the measuring point of the spindle as the input and the thermal error in Z-direction as the output, the thermal error prediction model is established by using GRU network. SSA is used to optimize the training parameters of GRU network, and finally a prediction model of SSA-GRU spindle Z-direction thermal error considering the influence of natural environment is established. The performance of the established model is verified by taking the test data of variable speed working condition as the robustness test set. The results show that SSA-GRU can be used for thermal error modeling and compensation, and the Z-direction thermal error of the machine tool spindle can be controlled within 8 μm, which has better prediction accuracy than the traditional network model.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-023-12276-2</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0268-3768 |
| ispartof | International journal of advanced manufacturing technology, 2023-10, Vol.128 (11-12), p.5519-5528 |
| issn | 0268-3768 1433-3015 |
| language | eng |
| recordid | cdi_proquest_journals_2870824222 |
| source | Springer LINK 全文期刊数据库 |
| subjects | Accuracy Advanced manufacturing technologies CAE) and Design Compensation Computer-Aided Engineering (CAD Cooling Deformation Engineering Error analysis Genetic algorithms Heat Industrial and Production Engineering Machine tools Manufacturing Mechanical Engineering Media Management Modelling Neural networks Original Article Prediction models Search algorithms Spindles Temperature distribution Time series |
| title | Thermal error modeling and compensation of spindle based on gate recurrent unit network |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T19%3A32%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Thermal%20error%20modeling%20and%20compensation%20of%20spindle%20based%20on%20gate%20recurrent%20unit%20network&rft.jtitle=International%20journal%20of%20advanced%20manufacturing%20technology&rft.au=Li,%20Yang&rft.date=2023-10-01&rft.volume=128&rft.issue=11-12&rft.spage=5519&rft.epage=5528&rft.pages=5519-5528&rft.issn=0268-3768&rft.eissn=1433-3015&rft_id=info:doi/10.1007/s00170-023-12276-2&rft_dat=%3Cproquest_cross%3E2870824222%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2870824222&rft_id=info:pmid/&rfr_iscdi=true |