Thermal error modeling with dirty and small training sample for the motorized spindle of a precision boring machine
- 84 Downloads
Data samples (temperature and thermal drifts) obtained in motorized spindle thermal experiments are usually small and contain much information. Some of the information cannot be fully comprehended by most regression modeling methods when modeling with small training samples; hence, the modeled thermal error predictors can seriously lack robustness, especially for the thermal tilt angle (vital for the boring accuracy of precision boring machines) predictors. To solve this problem, the LS-MLR (least-squares multivariable linear regression), the GA-SVR (genetic algorithm-support vector machine for regression), and the RFR (random forest regression) regression modeling methods are applied to construct the thermal error predictors (models) with the training sample, and the predictors are then evaluated with the testing sample. Comparisons of the three modeling methods are carried out afterwards; it is pointed out that predicting ability of the bias-corrected RFR predictor for thermal elongation is close to the GA-SVR predictor, and as for the thermal pitch and the thermal yaw, predicting ability of the RFR predictors are superior to the LS-MLR and the GA-SVR predictors. Finally, the evaluation results also indicate that the proposed RFR thermal error modeling method is promising to be used in motorized spindle thermal error predicting in machining processes even when the modeling data are poor.
KeywordsMotorized spindle Random forest regression Thermal elongation Thermal tilt angles Thermal error modeling
Unable to display preview. Download preview PDF.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 2.Aguirre G, Nanclares APD, Urreta H (2014) Thermal error compensation for large heavy duty milling-boring machines. Euspen Special Interest Group Meeting, Thermal IssuesGoogle Scholar
- 8.Han J, Wang L, Cheng N, Wang H, et al (2011) Thermal error modeling of machine tool based on fuzzy c-means cluster analysis. Electronic and Mechanical Engineering and Information Technology, 2011 International Conference on. IEEE, 2333–2336Google Scholar
- 11.Liang R, Ye W, Zhang HH, Yang Q (2012) The thermal error optimization models for CNC machine tools. Int J Adv Manuf Technol 63(9–12):1167–1176Google Scholar
- 13.Yang J, Feng B, Zhao L, Ma C, Mei X (2014) Thermal error modeling and compensation for a high-speed motorized spindle. Int J Adv Manuf Technol 77(5–8):1005–1017Google Scholar
- 16.Li XH (2013) Using“random forest”for classification and regression. Chin J Appl Entomol 50(4):1190–1197Google Scholar
- 22.B Efron (1992) Bootstrap methods, another look at the jackknife. Springer New YorkGoogle Scholar
- 24.Andrew AM (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge university pressGoogle Scholar
- 26.Kwok TY (1998) Support vector mixture for classification and regression problems//pattern recognition, 1998. Proceedings. Fourteenth International conference on. IEEE 1:255–258Google Scholar
- 27.Smola AJ (1996) Regression estimation with support vector learning machines. Master’s thesis, Technische Universit at M unchenGoogle Scholar
- 28.Michalewicz Z (1996) Genetic algorithms+ data structures = evolution programs. Springer Science & Business MediaGoogle Scholar
- 31.Johnson RA, Wichern DW (2001) Applied multivariate statistical analysis. Tsinghua University Press, Beijing, pp 321–342Google Scholar
- 32.J Yang (2014) Research on thermal behaviors and error compensation for machine tools [D]. Xi’an Jiaotong UniversityGoogle Scholar
- 33.Maj JR, Brence PD, Brown DE (2006) Analysis of robust measures in random forest regression. Dept. Syst. Inf. Eng., Univ. Virginia, Blacksburg, VA, USA, Tech. Rep. sie06_0002Google Scholar