Advertisement

Improvement of the regression model for spindle thermal elongation by a Boosting-based outliers detection approach

  • Mohan Lei
  • Gedong Jiang
  • Jun YangEmail author
  • Xuesong Mei
  • Ping Xia
  • Hu Shi
ORIGINAL ARTICLE
  • 83 Downloads

Abstract

Data-driven regression methods are adept at modeling the spindle thermal elongation for error reduction purposes, but are limited in their generalization ability for the established model to take effect under different work conditions, where one prime cause is that the measurement errors and environmental disturbances would inevitably lead to successive outliers in the experimental training data, and the regression algorithms tend to over-fit to such outliers in the thermal datasets which are usually small. Here, an approach is proposed for the first time to detect and remove the suspected outliers in the spindle thermal data while preserve most of the informative data at the same time, so that the disturbances from the outliers can be minimized. The proposed approach for outliers detection is a combination of Boosting and SVR (BSOD), where the support vector machine for regression (SVR) is used as the weak learner in Boosting. Furthermore, the SVR, which is now widely considered as the most suitable regression tool for mapping the temperature-thermal elongation relationship for a spindle, is adopted to regression model the spindle thermal elongation, where the genetic algorithm (GA) is employed to determine the optimal parameter setting for the modeling. Effectiveness of the BSOD approach was verified using four experimental datasets from two different spindles of precision boring machines under different work conditions. The results showed that the BSOD approach reduced the mean squared error MSE of the genetic algorithm optimized support vector machine estimation for unseen thermal elongation datasets in two cases by 66.09% and 49.13%, respectively. This research provides a feasible way to enhance the modeling quality for the thermal elongation of spindles during working process.

Keywords

Spindle Thermal elongation Boosting SVR Outlier detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Funding information

This work is supported by the National Natural Science Foundation of China (No. 51605375), Research Project of State Key Laboratory of Mechanical System and Vibration MSV201701, State Key Laboratory for Manufacturing Systems Engineering sklms2017007, the China Postdoctoral Science Foundation (No. 2015M582645, 2017T100741), Natural Science Foundation of Shaanxi (No. 2017JM5081), and The Central Universities fundamental research funds (No. xjj2017164).

References

  1. 1.
    Mayr J, Jedrzejewski J, Uhlmann E, Donmez MA, Knapp W, Hartig F, Wendt K, Moriwaki T, Shore P, Schmitt R, Brecher C, Wurz T, Wegener K (2013) Thermal issues in machine tools. CIRP Ann - Manuf Technol 61:771–791CrossRefGoogle Scholar
  2. 2.
    Cao H, Zhang X, Chen X (2017) The concept and progress of intelligent spindles:A review. Int J Mach Tools Manuf 112:21– 52CrossRefGoogle Scholar
  3. 3.
    Li B, Hong J, Tian X (2016) Generating optimal topologies for heat conduction by heat flow paths identification. Int J Mach Tools Manuf 75:177–182Google Scholar
  4. 4.
    Li Y, Zhao W, Lan S, Ni J, Wu W, Lu B (2015) A review on spindle thermal error compensation in machine tools. Int J Mach Tools Manuf 95:20–38CrossRefGoogle Scholar
  5. 5.
    Cheng Q, Qi Z, Zhang G, Zhao Y, Sun B, Gu P (2016) Robust modeling and prediction of thermally induced positional error based on grey rough set theory and neural networks. Int J Adv Manuf Technol 83:753–764CrossRefGoogle Scholar
  6. 6.
    Y Zhang J, Yang H (2012) Jiang, Machine tool thermal error modeling and prediction by grey neural network. Int J Adv Manuf Technol 59:1065–1072CrossRefGoogle Scholar
  7. 7.
    Huang Y, Zhang J, Li X, Tian L (2014) Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle. Int J Adv Manuf Technol 71:1669–1675CrossRefGoogle Scholar
  8. 8.
    Miao EM, Gong YY, Niu PC, Ji CZ, Chen HD (2013) Robustness of thermal error compensation modeling models of CNC machine tools. Int J Adv Manuf Technol 69:2593–2603CrossRefGoogle Scholar
  9. 9.
    Ustun B, Melssen WJ, Oudenhuijzen M, Buydens LMC (2005) Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization. Anal Chim Acta 544:292–305CrossRefGoogle Scholar
  10. 10.
    Yang J, Feng B, Zhao L, Ma C, Mei X (2015) Thermal error modeling and compensation for a high-speed motorized spindle. Int J Adv Manuf Technol 77:1005–1017CrossRefGoogle Scholar
  11. 11.
    Lo CH, Yuan J, Ni J (1995) An application of real-time error compensation on a turning center. Int J Mach Tools Manuf 35:1669–1682CrossRefGoogle Scholar
  12. 12.
    Lei M, Jiang G, Yang J, Mei X, Xia P, Zhao L (2017) Thermal error modeling with dirty and small training sample for the motorized spindle of a precision boring machine. Int J Adv Manuf Technol 93:571–586CrossRefGoogle Scholar
  13. 13.
    Miao EM, Liu Y, Liu H, Gao Z, Li W (2015) Study on the effects of changes in temperature-sensitive points on thermal error compensation model for CNC machine tool. Int J Mach Tools Manuf 97:50–59CrossRefGoogle Scholar
  14. 14.
    Liu H, Miao EM, Wei XY, Zhuang XD (2017) Obustness modeling method for thermal error of CNC machine tools based on ridge regression algorithm. Int J Mach Tools Manuf 113:35–48CrossRefGoogle Scholar
  15. 15.
    Liu K, Sun M, Zhu T, WU Y, Liu Y (2016) Modeling and compensation for spindle’s radial thermal drift error on a vertical machining center. Int J Mach Tools Manuf 105:58–67CrossRefGoogle Scholar
  16. 16.
    Hodge V, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 22:85–126CrossRefGoogle Scholar
  17. 17.
    Niu X, Shi S, Sun J, He X (2011) A survey of outlier detection methodologies and their applications, British Machine Vision Conference, 256-285 DBLPGoogle Scholar
  18. 18.
    Serneels S, Verdonck T (2009) Principal component regression for data containing outliers and missing elements. Comput Stat Data Anal 53:3855–3863MathSciNetCrossRefGoogle Scholar
  19. 19.
    Niu X, Shi S, Sun J, He X (2011) Robust regression and outlier detection with SVR: application to optic flow estimation, British Machine Vision Conference, 256-285 DBLPGoogle Scholar
  20. 20.
    Cheze N, Poggi JM (2006) Iterated Boosting for outlier detection. In: Batagelj, V, Bock, H-H, Ferligoj, A, žiberna, A (eds) Data science and classification. Springer Berlin Heidelberg, Berlin, pp 213–220Google Scholar
  21. 21.
    Smola AJ (2004) A tutorial on support vector regression. Kluwer Academic PublishersGoogle Scholar
  22. 22.
    Vapnik V (2004) The nature of statistical learning theory. Springer, BerlinzbMATHGoogle Scholar
  23. 23.
    Drucker H, Burges CJC, Kaufman L, Smola AJ, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst 28:213–220Google Scholar
  24. 24.
    Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  25. 25.
    Cherkassky V, Ma Y (2000) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17:113–126CrossRefGoogle Scholar
  26. 26.
    Drucker H (1997) Improving regressors using boosting techniques, Fourteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc, Burlington, pp 107–115Google Scholar
  27. 27.
    Solomatine DP, Shrestha DL (2004) Boosting: a Boosting algorithm for regression problems. IEEE International Joint Conference on Neural Networks. IEEE, pp 1163–1168Google Scholar
  28. 28.
    Valentini G, Dietterich TG (2004) Bias-variance analysis of s v machines for the d of SVM-based ensemble methods. J Mach Learn Res 5:725–775zbMATHGoogle Scholar
  29. 29.
    Michalewicz Z (1996) Genetic algorithms+ data structures= evolution programs. Springer Verlag, BerlinCrossRefGoogle Scholar
  30. 30.
    Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240CrossRefGoogle Scholar
  31. 31.
    Yang J, Mei X, Zhao L, Ma C, Feng B (2015) Thermal error compensation on a computer numerical control machine tool considering thermal tilt angles and cutting tool length. Proc Inst Mech Eng Part B J Eng Manuf 229:78–97CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Mohan Lei
    • 1
    • 2
  • Gedong Jiang
    • 1
    • 2
  • Jun Yang
    • 1
    • 2
    • 3
    Email author
  • Xuesong Mei
    • 1
    • 2
  • Ping Xia
    • 1
    • 2
  • Hu Shi
    • 1
    • 2
  1. 1.State Key Laboratory of Manufacturing Systems EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.Shaanxi Key Laboratory of Intelligent RobotsXi’an Jiaotong UniversityXi’anChina
  3. 3.State Key Laboratory of Mechanical System and VibrationShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations