Adaptive thermal error prediction for CNC machine tool spindle using online measurement and an improved recursive least square algorithm
Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machi...
Uložené v:
| Vydané v: | Case studies in thermal engineering Ročník 56; s. 104239 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.04.2024
Elsevier |
| Predmet: | |
| ISSN: | 2214-157X, 2214-157X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machine tools because training from static conditions leads to the inability to adapt to dynamic conditions. Therefore, an adaptive thermal error modeling method using online measurement and an improved recursive least square algorithm is proposed to fill this research gap, which updates the thermal error model adaptively to ensure that dynamic working conditions are learned in real time. Particularly, Spearman's rank correlation coefficient method is first adopted for temperature-sensitive point selection to capture the nonlinear relationship between temperature and thermal error variables. Furthermore, a variable-forgetting factor-based recursive least square (VFF-RLS) algorithm is proposed to improve the prediction performance, in which the proposed variable forgetting factor is adaptively updated according to real-time thermal error data collected by online measurement. The experimental results showed that the proposed VFF-RLS method can maintain a high prediction accuracy of 1.75 μm and robustness of 0.16 μm on both constant and dynamic working conditions. The effectiveness of the VFF-RLS method is validated by verification experiments. |
|---|---|
| AbstractList | Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machine tools because training from static conditions leads to the inability to adapt to dynamic conditions. Therefore, an adaptive thermal error modeling method using online measurement and an improved recursive least square algorithm is proposed to fill this research gap, which updates the thermal error model adaptively to ensure that dynamic working conditions are learned in real time. Particularly, Spearman's rank correlation coefficient method is first adopted for temperature-sensitive point selection to capture the nonlinear relationship between temperature and thermal error variables. Furthermore, a variable-forgetting factor-based recursive least square (VFF-RLS) algorithm is proposed to improve the prediction performance, in which the proposed variable forgetting factor is adaptively updated according to real-time thermal error data collected by online measurement. The experimental results showed that the proposed VFF-RLS method can maintain a high prediction accuracy of 1.75 μm and robustness of 0.16 μm on both constant and dynamic working conditions. The effectiveness of the VFF-RLS method is validated by verification experiments. |
| ArticleNumber | 104239 |
| Author | Ye, Honghan Hu, Weidong Wei, Xinyuan Wang, Gao |
| Author_xml | – sequence: 1 givenname: Xinyuan orcidid: 0000-0002-8633-9990 surname: Wei fullname: Wei, Xinyuan email: weixy@ahut.edu.cn organization: School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, 230009, China – sequence: 2 givenname: Honghan orcidid: 0000-0001-5329-7344 surname: Ye fullname: Ye, Honghan organization: Department of Statistics, School of Computer, Data & Information Sciences, College of Letters & Science, University of Wisconsin, Madison, WI, 53705, USA – sequence: 3 givenname: Gao surname: Wang fullname: Wang, Gao organization: School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, 230009, China – sequence: 4 givenname: Weidong surname: Hu fullname: Hu, Weidong organization: School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, 230009, China |
| BookMark | eNqFkc2OFCEUhStmTBzHeQI3vEC3UAVU1cLFpOPPJBPdaOKO8HPpplMF5YXuxDfwsYeaNsa40AXhcrnnEM73srmKKULTvGZ0yyiTb45bm0OBbUtbXju87cZnzXXbMr5hov929Uf9ornN-UgpZX03MM6vm593Ti8lnIGUA-CsJwKICcmC4IItIUXi63H3aUdmbQ8h1sGUJpKXEN0E5JRD3JMUp_VmBp1PCDPEQnR0dZEwL5jO4AiCPWFeH5rqVCH5-0kjED3tE4ZymF81z72eMtz-2m-ar-_ffdl93Dx8_nC_u3vYWM542YzegxedGFvPhfDC9YMA5kVvRN8a2ZmWC0O5c7WW0g3SOD7QjoGUdmR66G6a-4uvS_qoFgyzxh8q6aCeGgn3SmMJdgI1yDVLzgYzApd-MNqMUtb0pNGOUlO9uouXxZQzgv_tx6ha2aijemKjVjbqwqaqxr9UNhS9Rl1Qh-k_2rcXLdSIzgFQZRsg2gqrBlzqH8I_9Y_InK_f |
| CitedBy_id | crossref_primary_10_3390_lubricants13060269 crossref_primary_10_1007_s00170_025_16377_y crossref_primary_10_1007_s00170_025_15021_z crossref_primary_10_1016_j_ijmachtools_2025_104298 crossref_primary_10_1631_jzus_A2400287 crossref_primary_10_1016_j_measurement_2025_118389 crossref_primary_10_1016_j_icheatmasstransfer_2025_108977 crossref_primary_10_1038_s41598_024_77920_7 crossref_primary_10_1007_s00170_025_16433_7 crossref_primary_10_1016_j_measurement_2024_116341 crossref_primary_10_1016_j_ymssp_2025_112792 |
| Cites_doi | 10.1016/j.precisioneng.2021.10.007 10.1016/j.ymssp.2019.106397 10.3390/s22145085 10.1016/j.ijmachtools.2004.06.023 10.1016/j.precisioneng.2016.08.008 10.1016/j.csite.2022.102326 10.1016/j.measurement.2021.109891 10.1016/j.measurement.2023.112536 10.1016/j.ijmachtools.2021.103715 10.1016/j.cirp.2021.04.029 10.1016/j.cirp.2019.05.007 10.1016/j.ymssp.2019.106538 10.1016/j.csite.2022.102432 10.1016/j.measurement.2022.111121 10.1007/s40436-020-00342-x 10.1016/j.est.2023.107597 10.1016/j.precisioneng.2020.06.010 10.1016/j.cirp.2018.04.001 10.1016/j.cirp.2012.05.008 10.1016/j.cirpj.2019.04.002 10.1016/j.knosys.2021.107704 10.1016/j.mechmachtheory.2021.104639 10.1007/s00170-018-2918-5 10.1016/j.jmsy.2017.04.011 10.1109/TSP.2010.2040671 10.1016/j.csite.2023.103054 10.1016/j.precisioneng.2022.05.008 10.1016/j.ijmachtools.2016.10.005 |
| ContentType | Journal Article |
| Copyright | 2024 The Authors |
| Copyright_xml | – notice: 2024 The Authors |
| DBID | 6I. AAFTH AAYXX CITATION DOA |
| DOI | 10.1016/j.csite.2024.104239 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2214-157X |
| ExternalDocumentID | oai_doaj_org_article_864239418b9e46f8bab9660006bad00b 10_1016_j_csite_2024_104239 S2214157X24002703 |
| GroupedDBID | 0R~ 0SF 457 5VS 6I. AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAXUO ABMAC ACGFS ADBBV ADEZE AEXQZ AFTJW AGHFR AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ BCNDV EBS EJD FDB GROUPED_DOAJ HZ~ IPNFZ IXB KQ8 M41 M~E NCXOZ O9- OK1 RIG ROL SSZ AAYWO AAYXX ACVFH ADCNI ADVLN AEUPX AFJKZ AFPUW AIGII AKBMS AKYEP APXCP CITATION |
| ID | FETCH-LOGICAL-c414t-9ffef53592f455f5d785e1f57b572b63b245b04ddb6366d86bd48031e66c91a83 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 13 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001206741600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2214-157X |
| IngestDate | Fri Oct 03 12:51:49 EDT 2025 Wed Nov 05 20:52:40 EST 2025 Tue Nov 18 22:34:22 EST 2025 Sat Apr 13 16:37:45 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Thermal error modeling Variable forgetting factor Recursive least square algorithm CNC machine tools Adaptability |
| Language | English |
| License | This is an open access article under the CC BY license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c414t-9ffef53592f455f5d785e1f57b572b63b245b04ddb6366d86bd48031e66c91a83 |
| ORCID | 0000-0001-5329-7344 0000-0002-8633-9990 |
| OpenAccessLink | https://doaj.org/article/864239418b9e46f8bab9660006bad00b |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_864239418b9e46f8bab9660006bad00b crossref_primary_10_1016_j_csite_2024_104239 crossref_citationtrail_10_1016_j_csite_2024_104239 elsevier_sciencedirect_doi_10_1016_j_csite_2024_104239 |
| PublicationCentury | 2000 |
| PublicationDate | April 2024 2024-04-00 2024-04-01 |
| PublicationDateYYYYMMDD | 2024-04-01 |
| PublicationDate_xml | – month: 04 year: 2024 text: April 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Case studies in thermal engineering |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | Wang, Zhao (bib33) 2023 Fujishima, Narimatsu, Irino, Mori, Ibaraki (bib23) 2019; 25 Liu, Ma, Wang (bib6) 2020; 138 Liu, Du, Li, Deng, Feng, Yang (bib16) 2021; 9 Yang, Ni (bib5) 2005; 45 Li, Wang, Zhu, Wang, Zhu, Dai (bib12) 2022; 39 Chen, Chen, Xu (bib14) 2021; 184 Ma, Gui, Liu (bib36) 2021 Liu, Ma, Gui, Wang (bib17) 2022; 237 Volk, Groche, Brosius, Ghiotti, Kinsey, Liewald, Madej, Min, Yanagimoto (bib34) 2019; 68 Liu, Ma, Gui, Wang (bib9) 2022; 169 Wei, Ye, Feng (bib19) 2022; 22 Fu, Zhou, Zheng, Lu, Wang, Xie (bib25) 2022; 195 Xie, Wang, Zhang, Fan, Fernandez, Guerrero (bib31) 2023; 67 Liu, Ma, Wang (bib7) 2020; 135 Wei, Feng, Miao, Qian, Pan (bib27) 2022; 73 Mayr, Blaser, Ryser, Hernandez-Becerro (bib22) 2018; 67 Sun, Ji, Ren, Xie, Yan (bib30) 2019; 12 Mou, Liu (bib21) 1995; 117 Mayr, Blaser, Ryser, Hernandez-Becerro (bib35) 2018; 67 Mareš, Horejš, Havlík (bib10) 2020; 66 Wei, Ye, Miao, Pan (bib32) 2022; 77 Zhu, Yang, Feng, Du, Yang (bib15) 2022 Mayr, Jedrzejewski, Uhlmann, Alkan Donmez, Knapp, Härtig, Wendt, Moriwaki, Shore, Schmitt, Brecher, Würz, Wegener (bib1) 2012; 61 Cao, Zhang, Chen (bib2) 2017; 112 Weng, Gao, Zhang, Huang, Liu, Li, Zheng, Shi, Chang (bib8) 2021; 164 Wei, Miao, Liu, Liu, Chen (bib11) 2019; 101 (bib26) 2015 Blaser, Pavliček, Mori, Mayr, Weikert, Wegener (bib20) 2017; 44 Goel, Bernstein (bib29) 2018 Li, Wang, Zhu, Wang, Zhu (bib3) 2022; 38 Fu, Zheng, Zhou, Lu, Zhang, Wang, Wang (bib4) 2023; 210 Zimmermann, Breu, Mayr, Wegener (bib24) 2021; 70 Zhang, Gao, Yan (bib18) 2017; 47 Dai, Pang, Rui, Li, Wang, Li (bib13) 2023; 47 Skretting, Engan (bib28) 2010; 58 Zhang (10.1016/j.csite.2024.104239_bib18) 2017; 47 Fujishima (10.1016/j.csite.2024.104239_bib23) 2019; 25 (10.1016/j.csite.2024.104239_bib26) 2015 Liu (10.1016/j.csite.2024.104239_bib16) 2021; 9 Chen (10.1016/j.csite.2024.104239_bib14) 2021; 184 Dai (10.1016/j.csite.2024.104239_bib13) 2023; 47 Mareš (10.1016/j.csite.2024.104239_bib10) 2020; 66 Ma (10.1016/j.csite.2024.104239_bib36) 2021 Mayr (10.1016/j.csite.2024.104239_bib1) 2012; 61 Skretting (10.1016/j.csite.2024.104239_bib28) 2010; 58 Goel (10.1016/j.csite.2024.104239_bib29) 2018 Fu (10.1016/j.csite.2024.104239_bib4) 2023; 210 Wei (10.1016/j.csite.2024.104239_bib11) 2019; 101 Weng (10.1016/j.csite.2024.104239_bib8) 2021; 164 Blaser (10.1016/j.csite.2024.104239_bib20) 2017; 44 Liu (10.1016/j.csite.2024.104239_bib17) 2022; 237 Mayr (10.1016/j.csite.2024.104239_bib35) 2018; 67 Sun (10.1016/j.csite.2024.104239_bib30) 2019; 12 Wei (10.1016/j.csite.2024.104239_bib27) 2022; 73 Yang (10.1016/j.csite.2024.104239_bib5) 2005; 45 Mou (10.1016/j.csite.2024.104239_bib21) 1995; 117 Liu (10.1016/j.csite.2024.104239_bib9) 2022; 169 Mayr (10.1016/j.csite.2024.104239_bib22) 2018; 67 Wang (10.1016/j.csite.2024.104239_bib33) 2023 Liu (10.1016/j.csite.2024.104239_bib7) 2020; 135 Li (10.1016/j.csite.2024.104239_bib12) 2022; 39 Wei (10.1016/j.csite.2024.104239_bib19) 2022; 22 Li (10.1016/j.csite.2024.104239_bib3) 2022; 38 Xie (10.1016/j.csite.2024.104239_bib31) 2023; 67 Liu (10.1016/j.csite.2024.104239_bib6) 2020; 138 Volk (10.1016/j.csite.2024.104239_bib34) 2019; 68 Zimmermann (10.1016/j.csite.2024.104239_bib24) 2021; 70 Wei (10.1016/j.csite.2024.104239_bib32) 2022; 77 Cao (10.1016/j.csite.2024.104239_bib2) 2017; 112 Zhu (10.1016/j.csite.2024.104239_bib15) 2022 Fu (10.1016/j.csite.2024.104239_bib25) 2022; 195 |
| References_xml | – volume: 47 year: 2017 ident: bib18 article-title: Thermal error characteristic analysis and modeling for machine tools due to time-varying environmental temperature publication-title: Precis. Eng. – volume: 38 year: 2022 ident: bib3 article-title: Thermal error modeling of electrical spindle based on optimized ELM with marine predator algorithm publication-title: Case Stud. Therm. Eng. – year: 2015 ident: bib26 publication-title: ISO 230-3, Test Code for Machine Tools-Part 3: Determination of Thermal Effects – volume: 117 start-page: 389 year: 1995 end-page: 399 ident: bib21 article-title: An adaptive methodology for machine tool error correction publication-title: Journal of Manufacturing Science and Engineering, Transactions of the ASME – volume: 70 start-page: 431 year: 2021 end-page: 434 ident: bib24 article-title: Autonomously triggered model updates for self-learning thermal error compensation publication-title: CIRP Annals – volume: 25 start-page: 22 year: 2019 end-page: 25 ident: bib23 article-title: Adaptive thermal displacement compensation method based on deep learning publication-title: CIRP J Manuf Sci Technol – volume: 135 year: 2020 ident: bib7 article-title: Precision loss modeling method of ball screw pair publication-title: Mech. Syst. Signal Process. – volume: 164 year: 2021 ident: bib8 article-title: Analytical modelling method for thermal balancing design of machine tool structural components publication-title: Int J Mach Tools Manuf – volume: 237 year: 2022 ident: bib17 article-title: Transfer learning-based thermal error prediction and control with deep residual LSTM network publication-title: Knowl Based Syst – volume: 9 start-page: 235 year: 2021 end-page: 249 ident: bib16 article-title: Thermal error modeling based on BiLSTM deep learning for CNC machine tool publication-title: Adv. Manuf. – volume: 138 year: 2020 ident: bib6 article-title: Data-driven thermally-induced error compensation method of high-speed and precision five-axis machine tools publication-title: Mech. Syst. Signal Process. – volume: 66 year: 2020 ident: bib10 article-title: Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece publication-title: Precis. Eng. – volume: 61 start-page: 771 year: 2012 end-page: 791 ident: bib1 article-title: Thermal issues in machine tools publication-title: CIRP Ann Manuf Technol – volume: 169 year: 2022 ident: bib9 article-title: Simultaneous geometric and thermal error control of gear profile grinder based on analytical correlation between tooth surface error and position error of grinding wheel/workpiece publication-title: Mech Mach Theory – volume: 67 start-page: 551 year: 2018 end-page: 554 ident: bib22 article-title: An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates publication-title: CIRP Annals – start-page: 1 year: 2023 end-page: 25 ident: bib33 article-title: Three-stage feature selection approach for deep learning-based RUL prediction methods publication-title: Qual. Reliab. Eng. Int. – volume: 112 start-page: 21 year: 2017 end-page: 52 ident: bib2 article-title: The concept and progress of intelligent spindles: a review publication-title: Int J Mach Tools Manuf – volume: 22 start-page: 5085 year: 2022 ident: bib19 article-title: Year‐round thermal error modeling and compensation for the spindle of machine tools based on ambient temperature intervals publication-title: Sensors – volume: 67 year: 2023 ident: bib31 article-title: Improved lumped electrical characteristic modeling and adaptive forgetting factor recursive least squares-linearized particle swarm optimization full-parameter identification strategy for lithium-ion batteries considering the hysteresis component effect publication-title: J. Energy Storage – year: 2022 ident: bib15 article-title: Robust modeling method for thermal error of CNC machine tools based on random forest algorithm publication-title: J. Intell. Manuf. – volume: 58 start-page: 2121 year: 2010 end-page: 2130 ident: bib28 article-title: Recursive least squares dictionary learning algorithm publication-title: IEEE Trans. Signal Process. – volume: 12 year: 2019 ident: bib30 article-title: Adaptive forgetting factor recursive least square algorithm for online identification of equivalent circuit model parameters of a lithium-ion battery publication-title: Energies – volume: 195 year: 2022 ident: bib25 article-title: Improved unscented Kalman filter algorithm-based rapid identification of thermal errors of machine tool spindle for shortening thermal equilibrium time publication-title: Measurement – volume: 68 start-page: 775 year: 2019 end-page: 798 ident: bib34 article-title: Models and modelling for process limits in metal forming publication-title: CIRP Annals – volume: 67 start-page: 551 year: 2018 end-page: 554 ident: bib35 article-title: An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates publication-title: CIRP Annals – volume: 47 year: 2023 ident: bib13 article-title: Thermal error prediction model of high-speed motorized spindle based on DELM network optimized by weighted mean of vectors algorithm publication-title: Case Stud. Therm. Eng. – year: 2021 ident: bib36 article-title: Self learning-empowered thermal error control method of precision machine tools based on digital twin publication-title: J. Intell. Manuf. – volume: 101 start-page: 501 year: 2019 end-page: 509 ident: bib11 article-title: Two-dimensional thermal error compensation modeling for worktable of CNC machine tools publication-title: Int. J. Adv. Manuf. Technol. – volume: 210 year: 2023 ident: bib4 article-title: Look-ahead prediction of spindle thermal errors with on-machine measurement and the cubic exponential smoothing-unscented Kalman filtering-based temperature prediction model of the machine tools publication-title: Measurement – volume: 77 start-page: 65 year: 2022 end-page: 76 ident: bib32 article-title: Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools publication-title: Precis. Eng. – volume: 39 year: 2022 ident: bib12 article-title: Thermal error modeling of high-speed electric spindle based on Aquila Optimizer optimized least squares support vector machine publication-title: Case Stud. Therm. Eng. – volume: 45 start-page: 1 year: 2005 end-page: 11 ident: bib5 article-title: Adaptive model estimation of machine-tool thermal errors based on recursive dynamic modeling strategy publication-title: Int J Mach Tools Manuf – volume: 184 year: 2021 ident: bib14 article-title: A data-driven model for thermal error prediction considering thermoelasticity with gated recurrent unit attention publication-title: Measurement – volume: 44 start-page: 302 year: 2017 end-page: 309 ident: bib20 article-title: Adaptive learning control for thermal error compensation of 5-axis machine tools publication-title: J. Manuf. Syst. – year: 2018 ident: bib29 article-title: A targeted forgetting factor for recursive least squares publication-title: Proceedings of the IEEE Conference on Decision and Control – volume: 73 start-page: 313 year: 2022 end-page: 325 ident: bib27 article-title: Sub-regional thermal error compensation modeling for CNC machine tool worktables publication-title: Precis. Eng. – volume: 73 start-page: 313 year: 2022 ident: 10.1016/j.csite.2024.104239_bib27 article-title: Sub-regional thermal error compensation modeling for CNC machine tool worktables publication-title: Precis. Eng. doi: 10.1016/j.precisioneng.2021.10.007 – volume: 135 year: 2020 ident: 10.1016/j.csite.2024.104239_bib7 article-title: Precision loss modeling method of ball screw pair publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2019.106397 – volume: 22 start-page: 5085 year: 2022 ident: 10.1016/j.csite.2024.104239_bib19 article-title: Year‐round thermal error modeling and compensation for the spindle of machine tools based on ambient temperature intervals publication-title: Sensors doi: 10.3390/s22145085 – volume: 45 start-page: 1 year: 2005 ident: 10.1016/j.csite.2024.104239_bib5 article-title: Adaptive model estimation of machine-tool thermal errors based on recursive dynamic modeling strategy publication-title: Int J Mach Tools Manuf doi: 10.1016/j.ijmachtools.2004.06.023 – volume: 47 year: 2017 ident: 10.1016/j.csite.2024.104239_bib18 article-title: Thermal error characteristic analysis and modeling for machine tools due to time-varying environmental temperature publication-title: Precis. Eng. doi: 10.1016/j.precisioneng.2016.08.008 – volume: 38 year: 2022 ident: 10.1016/j.csite.2024.104239_bib3 article-title: Thermal error modeling of electrical spindle based on optimized ELM with marine predator algorithm publication-title: Case Stud. Therm. Eng. doi: 10.1016/j.csite.2022.102326 – volume: 184 year: 2021 ident: 10.1016/j.csite.2024.104239_bib14 article-title: A data-driven model for thermal error prediction considering thermoelasticity with gated recurrent unit attention publication-title: Measurement doi: 10.1016/j.measurement.2021.109891 – volume: 210 year: 2023 ident: 10.1016/j.csite.2024.104239_bib4 article-title: Look-ahead prediction of spindle thermal errors with on-machine measurement and the cubic exponential smoothing-unscented Kalman filtering-based temperature prediction model of the machine tools publication-title: Measurement doi: 10.1016/j.measurement.2023.112536 – year: 2022 ident: 10.1016/j.csite.2024.104239_bib15 article-title: Robust modeling method for thermal error of CNC machine tools based on random forest algorithm publication-title: J. Intell. Manuf. – volume: 164 year: 2021 ident: 10.1016/j.csite.2024.104239_bib8 article-title: Analytical modelling method for thermal balancing design of machine tool structural components publication-title: Int J Mach Tools Manuf doi: 10.1016/j.ijmachtools.2021.103715 – volume: 70 start-page: 431 year: 2021 ident: 10.1016/j.csite.2024.104239_bib24 article-title: Autonomously triggered model updates for self-learning thermal error compensation publication-title: CIRP Annals doi: 10.1016/j.cirp.2021.04.029 – volume: 68 start-page: 775 year: 2019 ident: 10.1016/j.csite.2024.104239_bib34 article-title: Models and modelling for process limits in metal forming publication-title: CIRP Annals doi: 10.1016/j.cirp.2019.05.007 – volume: 138 year: 2020 ident: 10.1016/j.csite.2024.104239_bib6 article-title: Data-driven thermally-induced error compensation method of high-speed and precision five-axis machine tools publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2019.106538 – volume: 39 year: 2022 ident: 10.1016/j.csite.2024.104239_bib12 article-title: Thermal error modeling of high-speed electric spindle based on Aquila Optimizer optimized least squares support vector machine publication-title: Case Stud. Therm. Eng. doi: 10.1016/j.csite.2022.102432 – volume: 195 year: 2022 ident: 10.1016/j.csite.2024.104239_bib25 article-title: Improved unscented Kalman filter algorithm-based rapid identification of thermal errors of machine tool spindle for shortening thermal equilibrium time publication-title: Measurement doi: 10.1016/j.measurement.2022.111121 – year: 2015 ident: 10.1016/j.csite.2024.104239_bib26 – volume: 9 start-page: 235 year: 2021 ident: 10.1016/j.csite.2024.104239_bib16 article-title: Thermal error modeling based on BiLSTM deep learning for CNC machine tool publication-title: Adv. Manuf. doi: 10.1007/s40436-020-00342-x – volume: 67 year: 2023 ident: 10.1016/j.csite.2024.104239_bib31 article-title: Improved lumped electrical characteristic modeling and adaptive forgetting factor recursive least squares-linearized particle swarm optimization full-parameter identification strategy for lithium-ion batteries considering the hysteresis component effect publication-title: J. Energy Storage doi: 10.1016/j.est.2023.107597 – volume: 66 year: 2020 ident: 10.1016/j.csite.2024.104239_bib10 article-title: Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece publication-title: Precis. Eng. doi: 10.1016/j.precisioneng.2020.06.010 – volume: 67 start-page: 551 year: 2018 ident: 10.1016/j.csite.2024.104239_bib35 article-title: An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates publication-title: CIRP Annals doi: 10.1016/j.cirp.2018.04.001 – volume: 61 start-page: 771 year: 2012 ident: 10.1016/j.csite.2024.104239_bib1 article-title: Thermal issues in machine tools publication-title: CIRP Ann Manuf Technol doi: 10.1016/j.cirp.2012.05.008 – year: 2021 ident: 10.1016/j.csite.2024.104239_bib36 article-title: Self learning-empowered thermal error control method of precision machine tools based on digital twin publication-title: J. Intell. Manuf. – volume: 25 start-page: 22 year: 2019 ident: 10.1016/j.csite.2024.104239_bib23 article-title: Adaptive thermal displacement compensation method based on deep learning publication-title: CIRP J Manuf Sci Technol doi: 10.1016/j.cirpj.2019.04.002 – volume: 237 year: 2022 ident: 10.1016/j.csite.2024.104239_bib17 article-title: Transfer learning-based thermal error prediction and control with deep residual LSTM network publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2021.107704 – start-page: 1 year: 2023 ident: 10.1016/j.csite.2024.104239_bib33 article-title: Three-stage feature selection approach for deep learning-based RUL prediction methods publication-title: Qual. Reliab. Eng. Int. – volume: 169 year: 2022 ident: 10.1016/j.csite.2024.104239_bib9 article-title: Simultaneous geometric and thermal error control of gear profile grinder based on analytical correlation between tooth surface error and position error of grinding wheel/workpiece publication-title: Mech Mach Theory doi: 10.1016/j.mechmachtheory.2021.104639 – volume: 101 start-page: 501 year: 2019 ident: 10.1016/j.csite.2024.104239_bib11 article-title: Two-dimensional thermal error compensation modeling for worktable of CNC machine tools publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-018-2918-5 – volume: 117 start-page: 389 year: 1995 ident: 10.1016/j.csite.2024.104239_bib21 article-title: An adaptive methodology for machine tool error correction publication-title: Journal of Manufacturing Science and Engineering, Transactions of the ASME – volume: 44 start-page: 302 year: 2017 ident: 10.1016/j.csite.2024.104239_bib20 article-title: Adaptive learning control for thermal error compensation of 5-axis machine tools publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2017.04.011 – volume: 58 start-page: 2121 year: 2010 ident: 10.1016/j.csite.2024.104239_bib28 article-title: Recursive least squares dictionary learning algorithm publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2010.2040671 – volume: 67 start-page: 551 year: 2018 ident: 10.1016/j.csite.2024.104239_bib22 article-title: An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates publication-title: CIRP Annals doi: 10.1016/j.cirp.2018.04.001 – volume: 47 year: 2023 ident: 10.1016/j.csite.2024.104239_bib13 article-title: Thermal error prediction model of high-speed motorized spindle based on DELM network optimized by weighted mean of vectors algorithm publication-title: Case Stud. Therm. Eng. doi: 10.1016/j.csite.2023.103054 – volume: 77 start-page: 65 year: 2022 ident: 10.1016/j.csite.2024.104239_bib32 article-title: Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools publication-title: Precis. Eng. doi: 10.1016/j.precisioneng.2022.05.008 – volume: 112 start-page: 21 year: 2017 ident: 10.1016/j.csite.2024.104239_bib2 article-title: The concept and progress of intelligent spindles: a review publication-title: Int J Mach Tools Manuf doi: 10.1016/j.ijmachtools.2016.10.005 – volume: 12 year: 2019 ident: 10.1016/j.csite.2024.104239_bib30 article-title: Adaptive forgetting factor recursive least square algorithm for online identification of equivalent circuit model parameters of a lithium-ion battery publication-title: Energies – year: 2018 ident: 10.1016/j.csite.2024.104239_bib29 article-title: A targeted forgetting factor for recursive least squares |
| SSID | ssj0001738144 |
| Score | 2.3608124 |
| Snippet | Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for... |
| SourceID | doaj crossref elsevier |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| StartPage | 104239 |
| SubjectTerms | Adaptability CNC machine tools Recursive least square algorithm Thermal error modeling Variable forgetting factor |
| Title | Adaptive thermal error prediction for CNC machine tool spindle using online measurement and an improved recursive least square algorithm |
| URI | https://dx.doi.org/10.1016/j.csite.2024.104239 https://doaj.org/article/864239418b9e46f8bab9660006bad00b |
| Volume | 56 |
| WOSCitedRecordID | wos001206741600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2214-157X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001738144 issn: 2214-157X databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2214-157X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001738144 issn: 2214-157X databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07b9swECaKoEM7BGnaIm6T4IaMNWJLJEWNjmEjQ2JkSAtvAl9yFdiWI7sdM-dn546SHU3OkkGCQJxIgnfiHe9O3zF2IbDVxz7uWt7HAwr9o5uqPCEg5FhHJrU-IDH9uUkmEzWdpnetUl-UE1bDA9cLd6kkQdTxvjKp5zJXRhsClERhMdr1eoZ2316Stg5TwbuSoCbifAszFBK6bAjH4mGfU2AzovLgLVUUEPtbGqmlZcZH7LAxD2FQT-sL--CXx-xzCzTwK3seOL2iTQrIdlsgta-qsoJVRTEXWmdAQxSGkyEsQqYkEpblHNarggAVgDLdZ1AjZMDi1UUIeunwgiK4GbyDilzxlN0OcyrwA-tHFCcPej4rq2Lzd_GN_R6P7ofX3aaeAjGCb7ppnvtcxCKNci5ELlyihO_nIjEiiYyMTcSF6XHn8FlKp6RxXOFH76W0aV-r-Ds7WJZLf8LAcikIys7FXlOoV4lEx5EVFjdQiz13WLRd2sw2YONU82KebbPKHrLAj4z4kdX86LBfu5dWNdbGfvIr4tmOlICyQwOKT9aIT_aW-HSY3HI8a2yO2pbArop9o_94j9F_sk_UZZ0JdMoONtU_f8Y-2v-bYl2dB5HG--3T6AXGDv1N |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Adaptive+thermal+error+prediction+for+CNC+machine+tool+spindle+using+online+measurement+and+an+improved+recursive+least+square+algorithm&rft.jtitle=Case+studies+in+thermal+engineering&rft.au=Wei%2C+Xinyuan&rft.au=Ye%2C+Honghan&rft.au=Wang%2C+Gao&rft.au=Hu%2C+Weidong&rft.date=2024-04-01&rft.pub=Elsevier+Ltd&rft.issn=2214-157X&rft.eissn=2214-157X&rft.volume=56&rft_id=info:doi/10.1016%2Fj.csite.2024.104239&rft.externalDocID=S2214157X24002703 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2214-157X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2214-157X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2214-157X&client=summon |