Robust modeling for thermal error of spindle of slant bed lathe based on error decomposition

Thermal error of spindle is critical to the slant bed CNC lathe towards high machining precision. The heat generated by the spindle itself contributes to the thermal error indeed. However, the thermal error is also influenced by the thermal deformation of other components such as the turret and lath...

Full description

Saved in:
Bibliographic Details
Published inCase studies in thermal engineering Vol. 51; p. 103564
Main Authors Shi, Hu, Qu, Qiangqiang, Mei, Xuesong, Tao, Tao, Wang, Haitao
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.11.2023
Subjects
Online AccessGet full text
ISSN2214-157X
2214-157X
DOI10.1016/j.csite.2023.103564

Cover

Loading…
Abstract Thermal error of spindle is critical to the slant bed CNC lathe towards high machining precision. The heat generated by the spindle itself contributes to the thermal error indeed. However, the thermal error is also influenced by the thermal deformation of other components such as the turret and lathe bed, especially in complex structured slant-bed CNC lathes where error coupling commonly exists. In order to achieve robust modeling of thermal error, a decomposition method was proposed in this paper to separate the contributions of spindle, turret and lathe bed from the overall measured thermal deformation. The thermal error coupling relationship was established from the perspective of machine tool construction. A specific testing scheme was designed to determine the parameters of decoupling model. Additionally, a comparative experiment was carried out by placing the sensor support horizontally and vertically to verify the effectiveness of thermal error decoupling. Taking T65-750 high-precision CNC lathe as the object, the relationship between thermal error and tilt angle in the X-direction of the headstock, bed, and other components was revealed, achieving robust prediction of thermal error irrespective of the ambient temperature subject to change. Finally, the thermal error model built based on error decomposition was applied to improve the machining accuracy of cylindrical parts dramatically in practice.
AbstractList Thermal error of spindle is critical to the slant bed CNC lathe towards high machining precision. The heat generated by the spindle itself contributes to the thermal error indeed. However, the thermal error is also influenced by the thermal deformation of other components such as the turret and lathe bed, especially in complex structured slant-bed CNC lathes where error coupling commonly exists. In order to achieve robust modeling of thermal error, a decomposition method was proposed in this paper to separate the contributions of spindle, turret and lathe bed from the overall measured thermal deformation. The thermal error coupling relationship was established from the perspective of machine tool construction. A specific testing scheme was designed to determine the parameters of decoupling model. Additionally, a comparative experiment was carried out by placing the sensor support horizontally and vertically to verify the effectiveness of thermal error decoupling. Taking T65-750 high-precision CNC lathe as the object, the relationship between thermal error and tilt angle in the X-direction of the headstock, bed, and other components was revealed, achieving robust prediction of thermal error irrespective of the ambient temperature subject to change. Finally, the thermal error model built based on error decomposition was applied to improve the machining accuracy of cylindrical parts dramatically in practice.
ArticleNumber 103564
Author Qu, Qiangqiang
Tao, Tao
Mei, Xuesong
Shi, Hu
Wang, Haitao
Author_xml – sequence: 1
  givenname: Hu
  orcidid: 0000-0003-2453-5969
  surname: Shi
  fullname: Shi, Hu
  email: tigershi@xjtu.edu.cn
– sequence: 2
  givenname: Qiangqiang
  surname: Qu
  fullname: Qu, Qiangqiang
– sequence: 3
  givenname: Xuesong
  surname: Mei
  fullname: Mei, Xuesong
– sequence: 4
  givenname: Tao
  surname: Tao
  fullname: Tao, Tao
– sequence: 5
  givenname: Haitao
  surname: Wang
  fullname: Wang, Haitao
BookMark eNqFkMtKQzEQhoNUsNY-gZvzAqfm1p5zFi6keIOCIAouhJDLRFPOSUoSBd_e9LIQF7qaf4b5hn_-UzTywQNC5wTPCCaLi_VMJ5dhRjFlZcLmC36ExpQSXpN58zL6oU_QNKU1xpg0rCWcj9HrY1AfKVdDMNA7_1bZEKv8DnGQfQUxli7YKm2cNz3sZC99rhSYqpdlr1IyFR38YdmADsMmFEMu-DN0bGWfYHqoE_R8c_20vKtXD7f3y6tVrRlvc90xzGWn8cJgRUFipg1RjBV_uGu1tIRKqzvFcdtgoFySRjHFLaMAtqGGsgnq9nd1DClFsEK7LLcOcpSuFwSLbVJiLXZJiW1SYp9UYdkvdhPdIOPXP9TlnoLy1qeDKJJ24DUYF0FnYYL7k_8G_9GGsQ
CitedBy_id crossref_primary_10_1007_s00170_023_12720_3
crossref_primary_10_1007_s00231_024_03519_3
crossref_primary_10_1007_s42452_024_06389_w
crossref_primary_10_1007_s10973_024_13889_9
crossref_primary_10_1007_s12541_024_01207_0
crossref_primary_10_1016_j_measurement_2025_117152
Cites_doi 10.1088/0957-0233/22/8/085107
10.1007/s00170-017-0353-7
10.4028/www.scientific.net/AMM.37-38.86
10.1016/j.csite.2022.102432
10.1016/j.csite.2023.103054
10.1080/10407782.2015.1037130
10.1016/j.ijmachtools.2006.02.018
10.3901/JME.2021.03.156
10.1007/s00170-015-7941-1
10.1016/S0890-6955(00)00010-9
10.1007/s00170-016-8868-x
ContentType Journal Article
Copyright 2023 The Authors
Copyright_xml – notice: 2023 The Authors
DBID 6I.
AAFTH
AAYXX
CITATION
DOI 10.1016/j.csite.2023.103564
DatabaseName ScienceDirect Open Access Titles
Elsevier:ScienceDirect:Open Access
CrossRef
DatabaseTitle CrossRef
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 2214-157X
ExternalDocumentID 10_1016_j_csite_2023_103564
S2214157X23008705
GroupedDBID 0R~
457
5VS
6I.
AACTN
AAEDT
AAEDW
AAFTH
AAIKJ
AALRI
AAXUO
ABMAC
ACGFS
ADBBV
ADEZE
ADVLN
AEXQZ
AFJKZ
AFTJW
AGHFR
AITUG
AKRWK
ALMA_UNASSIGNED_HOLDINGS
AMRAJ
BCNDV
EBS
EJD
FDB
GROUPED_DOAJ
HZ~
IPNFZ
IXB
KQ8
M41
M~E
O9-
OK1
RIG
ROL
SSZ
AAYWO
AAYXX
ACVFH
ADCNI
AEUPX
AFPUW
AIGII
AKBMS
AKYEP
APXCP
CITATION
ID FETCH-LOGICAL-c348t-9304a9c06d0b2ea03cd1b33144098caf12afc9b40870e24a17b3b4f32eef72d23
IEDL.DBID IXB
ISSN 2214-157X
IngestDate Tue Jul 01 02:28:41 EDT 2025
Thu Apr 24 22:51:21 EDT 2025
Sun Apr 06 06:53:53 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords Error decomposition
Thermal error
Error modeling
Spindle
Slant bed CNC lathe
Language English
License This is an open access article under the CC BY-NC-ND license.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c348t-9304a9c06d0b2ea03cd1b33144098caf12afc9b40870e24a17b3b4f32eef72d23
ORCID 0000-0003-2453-5969
OpenAccessLink https://www.sciencedirect.com/science/article/pii/S2214157X23008705
ParticipantIDs crossref_citationtrail_10_1016_j_csite_2023_103564
crossref_primary_10_1016_j_csite_2023_103564
elsevier_sciencedirect_doi_10_1016_j_csite_2023_103564
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate November 2023
2023-11-00
PublicationDateYYYYMMDD 2023-11-01
PublicationDate_xml – month: 11
  year: 2023
  text: November 2023
PublicationDecade 2020
PublicationTitle Case studies in thermal engineering
PublicationYear 2023
Publisher Elsevier Ltd
Publisher_xml – name: Elsevier Ltd
References Sun, Ren, Hong (bib14) 2017; 88
Zhang, Feng, Chen (bib11) 2013; 68
Li, Feng, Zhang (bib4) 2016; 69
Dai, Pang, Rui, Li, Wang, Li (bib3) July 2023; 47
Guo, Liu, Wang (bib8) 2016; 85
Xu, Jiang, Cai (bib5) 2007; 47
Yan, Xiao, Jun (bib9) 2018; 126
Ramesh, Mannan, Poo (bib1) 2000; 40
Bo, Yao, Kai (bib6) 2017
Liu, Miao, Wei (bib17) 2017
Mian, Fletcher, Longstaff (bib12) 2011; 22
Deng, Fu, He (bib10) 2010; 37–38
Wei, Ye, Miao (bib16) 2022
Liu, Han, Wang (bib2) 2021; 57
Li, Wang, Zhu, Wang, Zhu (bib7) 2022
Li, Li, Jiang (bib13) 2017; 92
Li, Wang, Zhu, Wang, Zhu, Dai (bib15) 2022; 39
Yan (10.1016/j.csite.2023.103564_bib9) 2018; 126
Li (10.1016/j.csite.2023.103564_bib15) 2022; 39
Li (10.1016/j.csite.2023.103564_bib7) 2022
Bo (10.1016/j.csite.2023.103564_bib6) 2017
Guo (10.1016/j.csite.2023.103564_bib8) 2016; 85
Deng (10.1016/j.csite.2023.103564_bib10) 2010; 37–38
Wei (10.1016/j.csite.2023.103564_bib16) 2022
Liu (10.1016/j.csite.2023.103564_bib17) 2017
Zhang (10.1016/j.csite.2023.103564_bib11) 2013; 68
Sun (10.1016/j.csite.2023.103564_bib14) 2017; 88
Ramesh (10.1016/j.csite.2023.103564_bib1) 2000; 40
Li (10.1016/j.csite.2023.103564_bib4) 2016; 69
Li (10.1016/j.csite.2023.103564_bib13) 2017; 92
Dai (10.1016/j.csite.2023.103564_bib3) 2023; 47
Xu (10.1016/j.csite.2023.103564_bib5) 2007; 47
Mian (10.1016/j.csite.2023.103564_bib12) 2011; 22
Liu (10.1016/j.csite.2023.103564_bib2) 2021; 57
References_xml – volume: 47
  year: July 2023
  ident: bib3
  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.
– volume: 68
  year: 2013
  ident: bib11
  article-title: A method for thermal performance modeling and simulation of machine tools[J]
  publication-title: Int. J. Adv. Des. Manuf. Technol.
– volume: 22
  year: 2011
  ident: bib12
  article-title: Efficient thermal error prediction in a machine tool using finite element analysis
  publication-title: Meas. Sci. Technol.
– start-page: 113
  year: 2017
  ident: bib17
  article-title: Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm[J]
  publication-title: Int. J. Mach. Tool Manufact.
– volume: 57
  start-page: 156
  year: 2021
  end-page: 173
  ident: bib2
  article-title: Review on thermal error compensation for axes of CNC machine tools
  publication-title: J. Mech. Eng.
– start-page: 38
  year: 2022
  ident: bib7
  article-title: Thermal error modeling of electrical spindle based on optimized ELM with marine predator algorithm[J]
  publication-title: Case Stud. Therm. Eng.
– volume: 126
  start-page: 324
  year: 2018
  end-page: 335
  ident: bib9
  article-title: Uneven heat generation and thermal performance of spindle bearings[J]
  publication-title: Tribol. Int.
– volume: 40
  year: 2000
  ident: bib1
  article-title: Error compensation in machine tools — a review
  publication-title: Int. J. Mach. Tool Manufact.
– volume: 37–38
  start-page: 86
  year: 2010
  end-page: 89
  ident: bib10
  article-title: An analysis of thermal-structural characteristics for precision linear rolling guide CNC grinding machine tool's bed[J]
  publication-title: Appl. Mech. Mater.
– start-page: 77
  year: 2022
  ident: bib16
  article-title: Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools[J]
  publication-title: Precis. Eng.
– volume: 39
  year: 2022
  ident: bib15
  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: 69
  start-page: 51
  year: 2016
  end-page: 66
  ident: bib4
  article-title: Method for modifying convective heat transfer coefficients used in the thermal simulation of a feed drive system based on the response surface methodology
  publication-title: Numer. Heat Tran. Part A: Applications
– volume: 85
  start-page: 227
  year: 2016
  end-page: 236
  ident: bib8
  article-title: Dynamic modeling and experimental verification for the feeding system of a gantry machine tool based on thermal-mechanical coupling
  publication-title: Int. J. Adv. Des. Manuf. Technol.
– volume: 92
  start-page: 3073
  year: 2017
  end-page: 3092
  ident: bib13
  article-title: Thermal error modeling and compensation of a heavy gantry-type machine tool and its verification in machining
  publication-title: Int. J. Adv. Des. Manuf. Technol.
– volume: 47
  start-page: 53
  year: 2007
  end-page: 62
  ident: bib5
  article-title: An improved thermal model for machine tool bearings
  publication-title: Int. J. Mach. Tool Manufact.
– start-page: 694
  year: 2017
  end-page: 697
  ident: bib6
  article-title: Analysis of thermal characteristics of spindle and headstock of machine tool based on finite element method
  publication-title: IEEE International Conference on Applied System Innovation
– volume: 88
  start-page: 1267
  year: 2017
  end-page: 1277
  ident: bib14
  article-title: Thermal error reduction based on thermodynamics structure optimization method for an ultra-precision machine tool
  publication-title: Int. J. Adv. Des. Manuf. Technol.
– start-page: 38
  year: 2022
  ident: 10.1016/j.csite.2023.103564_bib7
  article-title: Thermal error modeling of electrical spindle based on optimized ELM with marine predator algorithm[J]
  publication-title: Case Stud. Therm. Eng.
– volume: 22
  issue: 8
  year: 2011
  ident: 10.1016/j.csite.2023.103564_bib12
  article-title: Efficient thermal error prediction in a machine tool using finite element analysis
  publication-title: Meas. Sci. Technol.
  doi: 10.1088/0957-0233/22/8/085107
– volume: 92
  start-page: 3073
  issue: 9–12
  year: 2017
  ident: 10.1016/j.csite.2023.103564_bib13
  article-title: Thermal error modeling and compensation of a heavy gantry-type machine tool and its verification in machining
  publication-title: Int. J. Adv. Des. Manuf. Technol.
  doi: 10.1007/s00170-017-0353-7
– volume: 37–38
  start-page: 86
  year: 2010
  ident: 10.1016/j.csite.2023.103564_bib10
  article-title: An analysis of thermal-structural characteristics for precision linear rolling guide CNC grinding machine tool's bed[J]
  publication-title: Appl. Mech. Mater.
  doi: 10.4028/www.scientific.net/AMM.37-38.86
– volume: 39
  year: 2022
  ident: 10.1016/j.csite.2023.103564_bib15
  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: 126
  start-page: 324
  issue: 17
  year: 2018
  ident: 10.1016/j.csite.2023.103564_bib9
  article-title: Uneven heat generation and thermal performance of spindle bearings[J]
  publication-title: Tribol. Int.
– start-page: 113
  year: 2017
  ident: 10.1016/j.csite.2023.103564_bib17
  article-title: Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm[J]
  publication-title: Int. J. Mach. Tool Manufact.
– volume: 47
  year: 2023
  ident: 10.1016/j.csite.2023.103564_bib3
  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: 69
  start-page: 51
  issue: 1
  year: 2016
  ident: 10.1016/j.csite.2023.103564_bib4
  article-title: Method for modifying convective heat transfer coefficients used in the thermal simulation of a feed drive system based on the response surface methodology
  publication-title: Numer. Heat Tran. Part A: Applications
  doi: 10.1080/10407782.2015.1037130
– volume: 47
  start-page: 53
  issue: 1
  year: 2007
  ident: 10.1016/j.csite.2023.103564_bib5
  article-title: An improved thermal model for machine tool bearings
  publication-title: Int. J. Mach. Tool Manufact.
  doi: 10.1016/j.ijmachtools.2006.02.018
– start-page: 77
  year: 2022
  ident: 10.1016/j.csite.2023.103564_bib16
  article-title: Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools[J]
  publication-title: Precis. Eng.
– volume: 57
  start-page: 156
  year: 2021
  ident: 10.1016/j.csite.2023.103564_bib2
  article-title: Review on thermal error compensation for axes of CNC machine tools
  publication-title: J. Mech. Eng.
  doi: 10.3901/JME.2021.03.156
– volume: 85
  start-page: 227
  issue: 1
  year: 2016
  ident: 10.1016/j.csite.2023.103564_bib8
  article-title: Dynamic modeling and experimental verification for the feeding system of a gantry machine tool based on thermal-mechanical coupling
  publication-title: Int. J. Adv. Des. Manuf. Technol.
  doi: 10.1007/s00170-015-7941-1
– volume: 40
  issue: 9
  year: 2000
  ident: 10.1016/j.csite.2023.103564_bib1
  article-title: Error compensation in machine tools — a review
  publication-title: Int. J. Mach. Tool Manufact.
  doi: 10.1016/S0890-6955(00)00010-9
– start-page: 694
  year: 2017
  ident: 10.1016/j.csite.2023.103564_bib6
  article-title: Analysis of thermal characteristics of spindle and headstock of machine tool based on finite element method
  publication-title: IEEE International Conference on Applied System Innovation
– volume: 68
  issue: 5–8
  year: 2013
  ident: 10.1016/j.csite.2023.103564_bib11
  article-title: A method for thermal performance modeling and simulation of machine tools[J]
  publication-title: Int. J. Adv. Des. Manuf. Technol.
– volume: 88
  start-page: 1267
  issue: 5–8
  year: 2017
  ident: 10.1016/j.csite.2023.103564_bib14
  article-title: Thermal error reduction based on thermodynamics structure optimization method for an ultra-precision machine tool
  publication-title: Int. J. Adv. Des. Manuf. Technol.
  doi: 10.1007/s00170-016-8868-x
SSID ssj0001738144
Score 2.3190424
Snippet Thermal error of spindle is critical to the slant bed CNC lathe towards high machining precision. The heat generated by the spindle itself contributes to the...
SourceID crossref
elsevier
SourceType Enrichment Source
Index Database
Publisher
StartPage 103564
SubjectTerms Error decomposition
Error modeling
Slant bed CNC lathe
Spindle
Thermal error
Title Robust modeling for thermal error of spindle of slant bed lathe based on error decomposition
URI https://dx.doi.org/10.1016/j.csite.2023.103564
Volume 51
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFA9jJz2Inzi_yMGjZW2SpuaowzEEPaiDHoSSpAlMZju27v83L011guzgrQl50P4a3vsl7wuha55SLpgtozLJ3AGFWx0pa92ZB7xASklh_H3H0zOfTNljnuY9NOpyYSCsMuj-Vqd7bR1mhgHN4WI2G74Skjjrk-WORMdu10GiOVRqgSS-_P7nniVzNsn3dIX1EQh0xYd8mJf2TlpoIg755ylnfxuoDaMz3kd7gS3iu_aFDlDPVIdod6OG4BF6f6nVetVg39LGzWDHQjGwuk8naJZLN6otXi1mUE3BP84dmFiZEs-B_WGwYyWuq7C4NBBlHkK5jtF0_PA2mkShZUKk3bc3kaAxk0LHvIwVMTKmukwUpeDBFbda2oRIq4ViAJghTCaZoopZSoyxGSkJPUH9qq7MKcKpIy_MCAWEijFKZJbKRGobC8upVckAkQ6nQod64tDWYl50gWMfhQe3AHCLFtwBuvkWWrTlNLYv590PKH7tisIp_G2CZ_8VPEc7MGqzDS9Qv1muzaWjHY268sf1K7-7vgCs4NYv
linkProvider Elsevier
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFA9zHtSD-InzMwePlrVJmq5HHY5Ntx10gx2EkqQJTGY79vH_m5e2OkE8eGvTPCi_hPd-yftC6JaHlMfMpF4aRPaAwo3ypDH2zANeIClFrN19x2DIu2P2NAknNdSucmEgrLLU_YVOd9q6HGmWaDbn02nzlZDAWp9oYkm0b3dduIW2LRvgENfVmzx8X7RE1ii5pq4g4IFEVX3IxXkp56WFLuKQgB5y9ruF2rA6nQO0X9JFfF_80SGq6ewI7W0UETxGby-5XC9X2PW0sSPY0lAMtO7DCurFwr7lBi_nUyin4B5nFk0sdYpnQP8wGLIU51k5OdUQZl7Gcp2gcedx1O56Zc8ET1HWWnkx9ZmIlc9TXxItfKrSQFIKLty4pYQJiDAqlgwQ04SJIJJUMkOJ1iYiKaGnqJ7lmT5DOLTshelYAqNijBIRhSIQyvix4dTIoIFIhVOiyoLi0NdillSRY--JAzcBcJMC3Aa6-xKaF_U0_p7OqwVIfmyLxGr8vwTP_yt4g3a6o0E_6feGzxdoF74UqYeXqL5arPWV5SAree322CeC9dhh
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=Robust+modeling+for+thermal+error+of+spindle+of+slant+bed+lathe+based+on+error+decomposition&rft.jtitle=Case+studies+in+thermal+engineering&rft.au=Shi%2C+Hu&rft.au=Qu%2C+Qiangqiang&rft.au=Mei%2C+Xuesong&rft.au=Tao%2C+Tao&rft.date=2023-11-01&rft.pub=Elsevier+Ltd&rft.issn=2214-157X&rft.eissn=2214-157X&rft.volume=51&rft_id=info:doi/10.1016%2Fj.csite.2023.103564&rft.externalDocID=S2214157X23008705
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