Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm

For thermal error compensation technology of CNC machine tools, the collinearity between temperature sensitive points is the main factor for determining the predicted robustness of thermal error model. The temperature sensitive points are input variables of the thermal error model. This paper studie...

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Published in:	International journal of machine tools & manufacture Vol. 113; pp. 35 - 48
Main Authors:	Liu, Hui, Miao, En Ming, Wei, Xin Yuan, Zhuang, Xin Dong
Format:	Journal Article
Language:	English
Published:	Elmsford Elsevier Ltd 01.02.2017 Elsevier BV
Subjects:	Algorithms CNC machine tools Collinearity Correlation analysis Correlation coefficients Error analysis Error compensation Error detection Machine tools Mathematical models Modelling Numerical controls Predicted accuracy and robustness Regression Regression analysis Ridge regression Robustness (mathematics) Seasons Temperature Thermal error Three dimensional models Ridge regression Thermal error Collinearity Predicted accuracy and robustness CNC machine tools
ISSN:	0890-6955, 1879-2170
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Abstract	For thermal error compensation technology of CNC machine tools, the collinearity between temperature sensitive points is the main factor for determining the predicted robustness of thermal error model. The temperature sensitive points are input variables of the thermal error model. This paper studies the thermal error of Leaderway V-450 type CNC machine tools during different seasons. It is found that although the commonly used temperature sensitive point selection methods can significantly reduce the collinearity between temperature sensitive points, the correlation between some of the selected temperature-sensitive points and thermal error is weak. This causes the temperature-sensitive points to be variable and the predicted accuracy and robustness of thermal error to be reduced. Therefore, in this paper, the temperature sensitive points are selected directly by their correlation with thermal error to eliminate variability. However, the experimental results also show that the collinearity between temperature sensitive points is very large. Hence, the ridge regression algorithm is used to establish a thermal error model to inhibit the bad influence of collinearity on the thermal error predicted robustness. Thus, the “robustness ridge regression machine tool thermal error modeling method” is proposed, the “RRR method” for short. In addition, in the “RRR method”, the correlation coefficient is used to measure the correlation between temperature sensitive points and thermal error instead of the commonly used gray correlation; because this paper finds that the gray correlation algorithm is essentially inapplicable for measuring a negative correlation. Based on the thermal error experiment data for the whole year, the “RRR method” is compared with two currently used methods, and the results show that the “RRR method” can significantly enhance the long-term predicted accuracy and robustness of thermal error. Finally, the application effect of practical compensation shows that the “RRR method” is usable and effective. •The long-term prediction performance of commonly used traditional machine tool thermal error modeling methods is deeply studied.•Although the traditional methods can significantly reduce the collinearity between temperature sensitive points (Input variables of thermal error model), the correlation between some points and thermal error is weak.•The temperature sensitive points which have strong correlation with thermal error also have high collinearity between them.•The ridge regression algorithm is used to inhibit the bad influence of collinearity on the thermal error predicted robustness.•The thermal error predicted effect of proposed method is verified by 18 thermal error experiments done in different seasons.
AbstractList	For thermal error compensation technology of CNC machine tools, the collinearity between temperature sensitive points is the main factor for determining the predicted robustness of thermal error model. The temperature sensitive points are input variables of the thermal error model. This paper studies the thermal error of Leaderway V-450 type CNC machine tools during different seasons. It is found that although the commonly used temperature sensitive point selection methods can significantly reduce the collinearity between temperature sensitive points, the correlation between some of the selected temperature-sensitive points and thermal error is weak. This causes the temperature-sensitive points to be variable and the predicted accuracy and robustness of thermal error to be reduced. Therefore, in this paper, the temperature sensitive points are selected directly by their correlation with thermal error to eliminate variability. However, the experimental results also show that the collinearity between temperature sensitive points is very large. Hence, the ridge regression algorithm is used to establish a thermal error model to inhibit the bad influence of collinearity on the thermal error predicted robustness. Thus, the “robustness ridge regression machine tool thermal error modeling method” is proposed, the “RRR method” for short. In addition, in the “RRR method”, the correlation coefficient is used to measure the correlation between temperature sensitive points and thermal error instead of the commonly used gray correlation; because this paper finds that the gray correlation algorithm is essentially inapplicable for measuring a negative correlation. Based on the thermal error experiment data for the whole year, the “RRR method” is compared with two currently used methods, and the results show that the “RRR method” can significantly enhance the long-term predicted accuracy and robustness of thermal error. Finally, the application effect of practical compensation shows that the “RRR method” is usable and effective. •The long-term prediction performance of commonly used traditional machine tool thermal error modeling methods is deeply studied.•Although the traditional methods can significantly reduce the collinearity between temperature sensitive points (Input variables of thermal error model), the correlation between some points and thermal error is weak.•The temperature sensitive points which have strong correlation with thermal error also have high collinearity between them.•The ridge regression algorithm is used to inhibit the bad influence of collinearity on the thermal error predicted robustness.•The thermal error predicted effect of proposed method is verified by 18 thermal error experiments done in different seasons. For thermal error compensation technology of CNC machine tools, the collinearity between temperature sensitive points is the main factor for determining the predicted robustness of thermal error model. The temperature sensitive points are input variables of the thermal error model. This paper studies the thermal error of Leaderway V-450 type CNC machine tools during different seasons. It is found that although the commonly used temperature sensitive point selection methods can significantly reduce the collinearity between temperature sensitive points, the correlation between some of the selected temperature-sensitive points and thermal error is weak. This causes the temperature-sensitive points to be variable and the predicted accuracy and robustness of thermal error to be reduced. Therefore, in this paper, the temperature sensitive points are selected directly by their correlation with thermal error to eliminate variability. However, the experimental results also show that the collinearity between temperature sensitive points is very large. Hence, the ridge regression algorithm is used to establish a thermal error model to inhibit the bad influence of collinearity on the thermal error predicted robustness. Thus, the "robustness ridge regression machine tool thermal error modeling method" is proposed, the "RRR method" for short. In addition, in the "RRR method", the correlation coefficient is used to measure the correlation between temperature sensitive points and thermal error instead of the commonly used gray correlation; because this paper finds that the gray correlation algorithm is essentially inapplicable for measuring a negative correlation. Based on the thermal error experiment data for the whole year, the "RRR method" is compared with two currently used methods, and the results show that the "RRR method" can significantly enhance the long-term predicted accuracy and robustness of thermal error. Finally, the application effect of practical compensation shows that the "RRR method" is usable and effective.
Author	Liu, Hui Wei, Xin Yuan Zhuang, Xin Dong Miao, En Ming
Author_xml	– sequence: 1 givenname: Hui surname: Liu fullname: Liu, Hui – sequence: 2 givenname: En Ming surname: Miao fullname: Miao, En Ming email: miaoem@163.com – sequence: 3 givenname: Xin Yuan surname: Wei fullname: Wei, Xin Yuan – sequence: 4 givenname: Xin Dong surname: Zhuang fullname: Zhuang, Xin Dong
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Cites_doi	10.1016/j.ijmachtools.2011.04.001 10.1007/s00170-013-4998-6 10.1016/0167-8191(90)90086-O 10.1016/j.ijmachtools.2006.06.018 10.1080/00401706.2000.10485983 10.1007/s00170-013-5229-x 10.1016/j.ijmachtools.2014.03.002 10.1016/j.ijmachtools.2009.11.002 10.1007/s00170-011-3564-3 10.1007/s00170-014-6193-9 10.1007/s00521-007-0084-z 10.1016/S0141-6359(00)00044-1 10.1016/j.ijmachtools.2015.07.004 10.1016/0890-6955(95)00040-2 10.1016/S0924-0136(97)00291-4 10.1016/j.apm.2014.10.016 10.1016/j.neunet.2009.11.016 10.1016/S0007-8506(07)63001-7 10.1016/j.ijmachtools.2006.02.018 10.1007/s00170-008-1791-z 10.1080/01621459.1992.10475190 10.1016/0278-6125(96)82336-3 10.2307/1937887 10.1016/S0890-6955(00)00010-9 10.1016/j.precisioneng.2011.07.013 10.1016/j.procir.2014.01.080 10.1016/S0890-6955(02)00263-8 10.1016/S0890-6955(02)00264-X 10.1080/02664760500165339
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References	Mize, Ziegert (bib16) 2000; 24 Creighton, Honegger, Tulsian, Mukhopadhyay (bib10) 2010; 50 Vyroubal (bib11) 2012; 36 Zhang, Ye, Shan (bib28) 2015; 85 Yang, Yuan, Ni (bib3) 1996; 15 Ramesh, Mannan, Poo (bib17) 2000; 40 Yang (bib5) 1998 Miao, Liu, Liu, Gao, Li (bib29) 2015; 97 Socha, Blum (bib32) 2007; 23 Wang, Wang, Li (bib23) 2013; 69 ISO 230-3, Test Code for Machine Tools Part 3: Determination of Thermal Effects, ISO Copyright Office, Switzerland, 2007. Whitley, Starkweather, Bogart (bib33) 1990; 14 Xu, Jiang, Cai (bib8) 2007; 47 Farrar, Glauber (bib6) 1964; 49 Tan, Mao, Liu (bib26) 2014; 82–83 Hippert, Taylor (bib34) 2010; 23 Yan, Yang (bib22) 2009; 43 Raymond (bib39) 2005 Fox, Monette (bib38) 1992; 87 Ramesh, Mannan, Poo (bib18) 2003; 43 Park, Lee, Gwon (bib15) 2000; 24 Douglas Bonett (bib35) 2005; 32 Miao, Gong, Dang (bib24) 2014; 74 Yang, Shi, Feng (bib25) 2014; 17 Jiang, Fan, Yang (bib36) 2014; 75 Raymond (bib30) 2005 Abdulshahed, Longstaff, Fletcher (bib27) 2015; 39 Yang, Deng, Ren, Li, Dou (bib21) 2004; 15 Yang, Yuan, Ni (bib12) 1996; 36 Xu, Liu, Kim, Shin, Lyu (bib7) 2011; 51 Liang, Ye, Zhang, Yang (bib42) 2012; 63 Zhang, Yang, Jiang (bib20) 2012; 59 Yang, Van, Vanherck (bib14) 1999; 44 Kumar (bib41) 2006 Raymond (bib40) 2005 Aronson (bib2) 1996; 116 Bryan (bib1) 1990; 39 Ramesh, Mannan, Poo (bib19) 2003; 43 Miao, Gong, Niu, Ji, Chen (bib4) 2013; 69 Wang, Zhang, Moon (bib13) 1998; 75 Hoerl, Kennard (bib31) 1970; 42 Zhao, Yang, Shen (bib9) 2007; 47 Raymond (10.1016/j.ijmachtools.2016.11.001_bib30) 2005 Raymond (10.1016/j.ijmachtools.2016.11.001_bib40) 2005 Miao (10.1016/j.ijmachtools.2016.11.001_bib4) 2013; 69 Wang (10.1016/j.ijmachtools.2016.11.001_bib23) 2013; 69 Park (10.1016/j.ijmachtools.2016.11.001_bib15) 2000; 24 Yang (10.1016/j.ijmachtools.2016.11.001_bib12) 1996; 36 Xu (10.1016/j.ijmachtools.2016.11.001_bib8) 2007; 47 Hippert (10.1016/j.ijmachtools.2016.11.001_bib34) 2010; 23 Yan (10.1016/j.ijmachtools.2016.11.001_bib22) 2009; 43 Yang (10.1016/j.ijmachtools.2016.11.001_bib3) 1996; 15 Creighton (10.1016/j.ijmachtools.2016.11.001_bib10) 2010; 50 Kumar (10.1016/j.ijmachtools.2016.11.001_bib41) 2006 Ramesh (10.1016/j.ijmachtools.2016.11.001_bib18) 2003; 43 Whitley (10.1016/j.ijmachtools.2016.11.001_bib33) 1990; 14 Ramesh (10.1016/j.ijmachtools.2016.11.001_bib17) 2000; 40 Zhang (10.1016/j.ijmachtools.2016.11.001_bib28) 2015; 85 Socha (10.1016/j.ijmachtools.2016.11.001_bib32) 2007; 23 Yang (10.1016/j.ijmachtools.2016.11.001_bib21) 2004; 15 Fox (10.1016/j.ijmachtools.2016.11.001_bib38) 1992; 87 Liang (10.1016/j.ijmachtools.2016.11.001_bib42) 2012; 63 Yang (10.1016/j.ijmachtools.2016.11.001_bib5) 1998 Vyroubal (10.1016/j.ijmachtools.2016.11.001_bib11) 2012; 36 Mize (10.1016/j.ijmachtools.2016.11.001_bib16) 2000; 24 Douglas Bonett (10.1016/j.ijmachtools.2016.11.001_bib35) 2005; 32 Miao (10.1016/j.ijmachtools.2016.11.001_bib24) 2014; 74 Miao (10.1016/j.ijmachtools.2016.11.001_bib29) 2015; 97 Farrar (10.1016/j.ijmachtools.2016.11.001_bib6) 1964; 49 Ramesh (10.1016/j.ijmachtools.2016.11.001_bib19) 2003; 43 Zhang (10.1016/j.ijmachtools.2016.11.001_bib20) 2012; 59 Abdulshahed (10.1016/j.ijmachtools.2016.11.001_bib27) 2015; 39 Yang (10.1016/j.ijmachtools.2016.11.001_bib25) 2014; 17 Raymond (10.1016/j.ijmachtools.2016.11.001_bib39) 2005 Zhao (10.1016/j.ijmachtools.2016.11.001_bib9) 2007; 47 Aronson (10.1016/j.ijmachtools.2016.11.001_bib2) 1996; 116 Tan (10.1016/j.ijmachtools.2016.11.001_bib26) 2014; 82–83 Bryan (10.1016/j.ijmachtools.2016.11.001_bib1) 1990; 39 Wang (10.1016/j.ijmachtools.2016.11.001_bib13) 1998; 75 Hoerl (10.1016/j.ijmachtools.2016.11.001_bib31) 1970; 42 Xu (10.1016/j.ijmachtools.2016.11.001_bib7) 2011; 51 Jiang (10.1016/j.ijmachtools.2016.11.001_bib36) 2014; 75 10.1016/j.ijmachtools.2016.11.001_bib37 Yang (10.1016/j.ijmachtools.2016.11.001_bib14) 1999; 44
References_xml	– start-page: 164 year: 2006 end-page: 204 ident: bib41 article-title: Neural Networks – volume: 36 start-page: 121 year: 2012 end-page: 127 ident: bib11 article-title: Compensation of machine tool thermal deformation in spindle axis direction based on decomposition method publication-title: Precis. Eng. – volume: 39 start-page: 1837 year: 2015 end-page: 1852 ident: bib27 article-title: Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera publication-title: Appl. Math. Model. – start-page: 91 year: 2005 end-page: 93 ident: bib40 publication-title: Classical and Modern Regression with Applications – volume: 47 start-page: 53 year: 2007 end-page: 62 ident: bib8 article-title: An improved thermal model for machine tool bearings publication-title: Int. J. Mach. Tools Manuf. – volume: 85 start-page: 2761 year: 2015 end-page: 2771 ident: bib28 article-title: Application of sliced inverse regression with fuzzy clustering for thermal error modeling of CNC machine tool publication-title: Int. J. Adv. Manuf. Technol. – volume: 69 start-page: 121 year: 2013 end-page: 126 ident: bib23 article-title: Thermal sensor selection for the thermal error modeling of machine tool based on the fuzzy clustering method publication-title: Int. J. Adv. Manuf. Technol. – volume: 43 start-page: 1124 year: 2009 end-page: 1132 ident: bib22 article-title: Application of synthetic gray correlation theory on thermal point optimization for machine tool thermal error compensation publication-title: Int. J. Adv. Manuf. Technol. – volume: 44 start-page: 146 year: 1999 end-page: 152 ident: bib14 article-title: Linear regression and neural net models applied to thermal error compensation in machine tools publication-title: Eur. J. Mech. Environ. Eng. – volume: 42 start-page: 80 year: 1970 end-page: 86 ident: bib31 article-title: Ridge regression: Biased estimation for nonorthogonal problems publication-title: Technometrics – start-page: 368 year: 2005 end-page: 370 ident: bib39 publication-title: Classical and Modern Regression with Applications – volume: 116 start-page: 45 year: 1996 end-page: 50 ident: bib2 article-title: War against thermal expansion publication-title: Manuf. Eng. – volume: 47 start-page: 1003 year: 2007 end-page: 1010 ident: bib9 article-title: Simulation of thermal behavior of a cnc machine tool spindle publication-title: Int. J. Mach. Tools Manuf. – volume: 43 start-page: 391 year: 2003 end-page: 404 ident: bib18 article-title: Thermal error measurement and modelling in machine tools.: Part I publication-title: Int. J. Mach. Tools Manuf. – volume: 74 start-page: 681 year: 2014 end-page: 691 ident: bib24 article-title: Temperature-sensitive point selection of thermal error model of CNC machining center publication-title: Int. J. Adv. Manuf. Technol. – volume: 23 start-page: 235 year: 2007 end-page: 247 ident: bib32 article-title: An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training publication-title: Neural Comput. Appl. – year: 1998 ident: bib5 article-title: Error Synthetic Compensation Technique and Application for NC Machine Tools – volume: 59 start-page: 1065 year: 2012 end-page: 1072 ident: bib20 article-title: Machine tool thermal error modeling and prediction by gray neural network publication-title: Int. J. Adv. Manuf. Technol. – volume: 51 start-page: 605 year: 2011 end-page: 611 ident: bib7 article-title: Thermal error forecast and performance evaluation for an air-cooling ball screw system publication-title: Int. J. Mach. Tools Manuf. – volume: 97 start-page: 50 year: 2015 end-page: 59 ident: bib29 article-title: Study on the effects of changes in temperature-sensitive points on thermal error compensation model for CNC machine tool publication-title: Int. J. Mach. Tools Manuf. – reference: ISO 230-3, Test Code for Machine Tools Part 3: Determination of Thermal Effects, ISO Copyright Office, Switzerland, 2007. – volume: 82–83 start-page: 11 year: 2014 end-page: 20 ident: bib26 article-title: A thermal error model for large machine tools that considers environmental thermal hysteresis effects publication-title: Int. J. Mach. Tools Manuf. – volume: 17 start-page: 698 year: 2014 end-page: 703 ident: bib25 article-title: Applying neural network based on fuzzy cluster pre-processing to thermal error modeling for coordinate boring machine publication-title: Procedia Cirp – volume: 75 start-page: 45 year: 1998 end-page: 53 ident: bib13 article-title: Compensation for the thermal error of a multi-axis machining center publication-title: J. Mater. Process. Technol. – volume: 14 start-page: 347 year: 1990 end-page: 361 ident: bib33 article-title: Genetic algorithms and neural networks: optimizing connections and connectivity publication-title: Parallel Comput. – volume: 75 start-page: 1279 year: 2014 end-page: 1289 ident: bib36 article-title: An improved method for thermally induced positioning errors measurement, modeling, and compensation publication-title: Int. J. Adv. Manuf. Technol. – volume: 24 start-page: 338 year: 2000 end-page: 346 ident: bib16 article-title: Neural network thermal error compensation of a machining center publication-title: Precis. Eng. – volume: 23 start-page: 386 year: 2010 end-page: 395 ident: bib34 article-title: An evaluation of bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting publication-title: Neural Netw. Off. J. Int. Neural Netw. Soc. – volume: 39 start-page: 645 year: 1990 end-page: 656 ident: bib1 article-title: International status of thermal error research publication-title: Ann. Cirp. – volume: 40 start-page: 1257 year: 2000 end-page: 1284 ident: bib17 article-title: Error compensation in machine tools – A review. Part II: thermal errors publication-title: Int. J. Mach. Tools Manuf. – volume: 32 start-page: 1089 year: 2005 end-page: 1094 ident: bib35 article-title: Robust confidence interval for a residual standard deviation publication-title: J. Appl. Stat. – volume: 63 start-page: 1167 year: 2012 end-page: 1176 ident: bib42 article-title: The thermal error optimization models for cnc machine tools publication-title: Int. J. Adv. Manuf. Technol. – volume: 49 start-page: 92 year: 1964 end-page: 107 ident: bib6 article-title: Multicollinearity in regression analysis: the problem revisited publication-title: Rev. Econ. Stat. – volume: 36 start-page: 527 year: 1996 end-page: 537 ident: bib12 article-title: The improvement of thermal error modeling and compensation on machine tools by CMAC neural network publication-title: Int. J. Mach. Tools Manuf. – volume: 15 year: 2004 ident: bib21 article-title: Grouping optimization modeling by selection of temperature variables for the thermal error compensation on machine tools publication-title: Chin. J. Mech. Eng. – start-page: 286 year: 2005 end-page: 289 ident: bib30 publication-title: Classical and Modern Regression with Applications – volume: 15 start-page: 113 year: 1996 end-page: 124 ident: bib3 article-title: Accuracy enhancement of a horizontal machining center by real-time error compensation publication-title: J. Manuf. Syst. – volume: 87 start-page: 178 year: 1992 end-page: 183 ident: bib38 article-title: Generalized collinearity diagnostics publication-title: J. Am. Stat. Assoc. – volume: 43 start-page: 405 year: 2003 end-page: 419 ident: bib19 article-title: Thermal error measurement and modelling in machine tools. Part II. hybrid Bayesian network - support vector machine model publication-title: Int. J. Mach. Tools Manuf. – volume: 69 start-page: 2593 year: 2013 end-page: 2603 ident: bib4 article-title: Robustness of thermal error compensation modeling models of CNC machine tools publication-title: Int. J. Adv. Manuf. Technol. – volume: 24 start-page: 1343 year: 2000 end-page: 1351 ident: bib15 article-title: Thermal error measurement and modeling techniques for the 5 degree of freedom (DOF) spindle unit drifts in CNC machine tools publication-title: Trans. Korean Soc. Mech. Eng. A – volume: 50 start-page: 386 year: 2010 end-page: 393 ident: bib10 article-title: Analysis of thermal errors in a high-speed micro-milling spindle publication-title: Int. J. Mach. Tools Manuf. – volume: 51 start-page: 605 issue: 7–8 year: 2011 ident: 10.1016/j.ijmachtools.2016.11.001_bib7 article-title: Thermal error forecast and performance evaluation for an air-cooling ball screw system publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/j.ijmachtools.2011.04.001 – volume: 69 start-page: 121 issue: 1–4 year: 2013 ident: 10.1016/j.ijmachtools.2016.11.001_bib23 article-title: Thermal sensor selection for the thermal error modeling of machine tool based on the fuzzy clustering method publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-013-4998-6 – volume: 14 start-page: 347 issue: 3 year: 1990 ident: 10.1016/j.ijmachtools.2016.11.001_bib33 article-title: Genetic algorithms and neural networks: optimizing connections and connectivity publication-title: Parallel Comput. doi: 10.1016/0167-8191(90)90086-O – volume: 116 start-page: 45 issue: 6 year: 1996 ident: 10.1016/j.ijmachtools.2016.11.001_bib2 article-title: War against thermal expansion publication-title: Manuf. Eng. – volume: 47 start-page: 1003 issue: 6 year: 2007 ident: 10.1016/j.ijmachtools.2016.11.001_bib9 article-title: Simulation of thermal behavior of a cnc machine tool spindle publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/j.ijmachtools.2006.06.018 – volume: 74 start-page: 681 issue: 5–8 year: 2014 ident: 10.1016/j.ijmachtools.2016.11.001_bib24 article-title: Temperature-sensitive point selection of thermal error model of CNC machining center publication-title: Int. J. Adv. Manuf. Technol. – start-page: 286 year: 2005 ident: 10.1016/j.ijmachtools.2016.11.001_bib30 – volume: 42 start-page: 80 issue: 1 year: 1970 ident: 10.1016/j.ijmachtools.2016.11.001_bib31 article-title: Ridge regression: Biased estimation for nonorthogonal problems publication-title: Technometrics doi: 10.1080/00401706.2000.10485983 – volume: 69 start-page: 2593 issue: 9 year: 2013 ident: 10.1016/j.ijmachtools.2016.11.001_bib4 article-title: Robustness of thermal error compensation modeling models of CNC machine tools publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-013-5229-x – volume: 82–83 start-page: 11 issue: 7 year: 2014 ident: 10.1016/j.ijmachtools.2016.11.001_bib26 article-title: A thermal error model for large machine tools that considers environmental thermal hysteresis effects publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/j.ijmachtools.2014.03.002 – volume: 50 start-page: 386 issue: 4 year: 2010 ident: 10.1016/j.ijmachtools.2016.11.001_bib10 article-title: Analysis of thermal errors in a high-speed micro-milling spindle publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/j.ijmachtools.2009.11.002 – volume: 59 start-page: 1065 issue: 9–12 year: 2012 ident: 10.1016/j.ijmachtools.2016.11.001_bib20 article-title: Machine tool thermal error modeling and prediction by gray neural network publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-011-3564-3 – volume: 75 start-page: 1279 issue: 9–12 year: 2014 ident: 10.1016/j.ijmachtools.2016.11.001_bib36 article-title: An improved method for thermally induced positioning errors measurement, modeling, and compensation publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-014-6193-9 – volume: 23 start-page: 235 issue: 16 year: 2007 ident: 10.1016/j.ijmachtools.2016.11.001_bib32 article-title: An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training publication-title: Neural Comput. Appl. doi: 10.1007/s00521-007-0084-z – volume: 24 start-page: 338 issue: 4 year: 2000 ident: 10.1016/j.ijmachtools.2016.11.001_bib16 article-title: Neural network thermal error compensation of a machining center publication-title: Precis. Eng. doi: 10.1016/S0141-6359(00)00044-1 – year: 1998 ident: 10.1016/j.ijmachtools.2016.11.001_bib5 – volume: 85 start-page: 2761 issue: 9 year: 2015 ident: 10.1016/j.ijmachtools.2016.11.001_bib28 article-title: Application of sliced inverse regression with fuzzy clustering for thermal error modeling of CNC machine tool publication-title: Int. J. Adv. Manuf. Technol. – volume: 97 start-page: 50 year: 2015 ident: 10.1016/j.ijmachtools.2016.11.001_bib29 article-title: Study on the effects of changes in temperature-sensitive points on thermal error compensation model for CNC machine tool publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/j.ijmachtools.2015.07.004 – volume: 36 start-page: 527 issue: 4 year: 1996 ident: 10.1016/j.ijmachtools.2016.11.001_bib12 article-title: The improvement of thermal error modeling and compensation on machine tools by CMAC neural network publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/0890-6955(95)00040-2 – ident: 10.1016/j.ijmachtools.2016.11.001_bib37 – volume: 75 start-page: 45 issue: 1–3 year: 1998 ident: 10.1016/j.ijmachtools.2016.11.001_bib13 article-title: Compensation for the thermal error of a multi-axis machining center publication-title: J. Mater. Process. Technol. doi: 10.1016/S0924-0136(97)00291-4 – volume: 44 start-page: 146 year: 1999 ident: 10.1016/j.ijmachtools.2016.11.001_bib14 article-title: Linear regression and neural net models applied to thermal error compensation in machine tools publication-title: Eur. J. Mech. Environ. Eng. – volume: 39 start-page: 1837 issue: 7 year: 2015 ident: 10.1016/j.ijmachtools.2016.11.001_bib27 article-title: Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2014.10.016 – volume: 23 start-page: 386 issue: 3 year: 2010 ident: 10.1016/j.ijmachtools.2016.11.001_bib34 article-title: An evaluation of bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting publication-title: Neural Netw. Off. J. Int. Neural Netw. Soc. doi: 10.1016/j.neunet.2009.11.016 – volume: 39 start-page: 645 issue: 2 year: 1990 ident: 10.1016/j.ijmachtools.2016.11.001_bib1 article-title: International status of thermal error research publication-title: Ann. Cirp. doi: 10.1016/S0007-8506(07)63001-7 – volume: 47 start-page: 53 issue: 1 year: 2007 ident: 10.1016/j.ijmachtools.2016.11.001_bib8 article-title: An improved thermal model for machine tool bearings publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/j.ijmachtools.2006.02.018 – volume: 43 start-page: 1124 issue: 11–12 year: 2009 ident: 10.1016/j.ijmachtools.2016.11.001_bib22 article-title: Application of synthetic gray correlation theory on thermal point optimization for machine tool thermal error compensation publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-008-1791-z – volume: 87 start-page: 178 issue: 417 year: 1992 ident: 10.1016/j.ijmachtools.2016.11.001_bib38 article-title: Generalized collinearity diagnostics publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1992.10475190 – volume: 15 start-page: 113 issue: 2 year: 1996 ident: 10.1016/j.ijmachtools.2016.11.001_bib3 article-title: Accuracy enhancement of a horizontal machining center by real-time error compensation publication-title: J. Manuf. Syst. doi: 10.1016/0278-6125(96)82336-3 – start-page: 91 year: 2005 ident: 10.1016/j.ijmachtools.2016.11.001_bib40 – volume: 63 start-page: 1167 issue: 9–12 year: 2012 ident: 10.1016/j.ijmachtools.2016.11.001_bib42 article-title: The thermal error optimization models for cnc machine tools publication-title: Int. J. Adv. Manuf. Technol. – volume: 49 start-page: 92 issue: 1 year: 1964 ident: 10.1016/j.ijmachtools.2016.11.001_bib6 article-title: Multicollinearity in regression analysis: the problem revisited publication-title: Rev. Econ. Stat. doi: 10.2307/1937887 – volume: 40 start-page: 1257 issue: 9 year: 2000 ident: 10.1016/j.ijmachtools.2016.11.001_bib17 article-title: Error compensation in machine tools – A review. Part II: thermal errors publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/S0890-6955(00)00010-9 – volume: 36 start-page: 121 issue: 1 year: 2012 ident: 10.1016/j.ijmachtools.2016.11.001_bib11 article-title: Compensation of machine tool thermal deformation in spindle axis direction based on decomposition method publication-title: Precis. Eng. doi: 10.1016/j.precisioneng.2011.07.013 – volume: 15 issue: 6 year: 2004 ident: 10.1016/j.ijmachtools.2016.11.001_bib21 article-title: Grouping optimization modeling by selection of temperature variables for the thermal error compensation on machine tools publication-title: Chin. J. Mech. Eng. – volume: 17 start-page: 698 year: 2014 ident: 10.1016/j.ijmachtools.2016.11.001_bib25 article-title: Applying neural network based on fuzzy cluster pre-processing to thermal error modeling for coordinate boring machine publication-title: Procedia Cirp doi: 10.1016/j.procir.2014.01.080 – volume: 43 start-page: 391 issue: 4 year: 2003 ident: 10.1016/j.ijmachtools.2016.11.001_bib18 article-title: Thermal error measurement and modelling in machine tools.: Part I publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/S0890-6955(02)00263-8 – volume: 43 start-page: 405 issue: 4 year: 2003 ident: 10.1016/j.ijmachtools.2016.11.001_bib19 article-title: Thermal error measurement and modelling in machine tools. Part II. hybrid Bayesian network - support vector machine model publication-title: Int. J. Mach. Tools Manuf. doi: 10.1016/S0890-6955(02)00264-X – start-page: 368 year: 2005 ident: 10.1016/j.ijmachtools.2016.11.001_bib39 – volume: 32 start-page: 1089 issue: 10 year: 2005 ident: 10.1016/j.ijmachtools.2016.11.001_bib35 article-title: Robust confidence interval for a residual standard deviation publication-title: J. Appl. Stat. doi: 10.1080/02664760500165339 – volume: 24 start-page: 1343 issue: 5 year: 2000 ident: 10.1016/j.ijmachtools.2016.11.001_bib15 article-title: Thermal error measurement and modeling techniques for the 5 degree of freedom (DOF) spindle unit drifts in CNC machine tools publication-title: Trans. Korean Soc. Mech. Eng. A – start-page: 164 year: 2006 ident: 10.1016/j.ijmachtools.2016.11.001_bib41
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SubjectTerms	Algorithms CNC machine tools Collinearity Correlation analysis Correlation coefficients Error analysis Error compensation Error detection Machine tools Mathematical models Modelling Numerical controls Predicted accuracy and robustness Regression Regression analysis Ridge regression Robustness (mathematics) Seasons Temperature Thermal error Three dimensional models
Title	Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm
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