Optimization of Measuring Points for High-Speed Motorized Spindle Thermal Error

Wei Dong Gou; Xin Wei Ye; Chun Li Lei; Zhi Yuan Rui

doi:10.4028/www.scientific.net/AMR.538-541.2113

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 538-541Optimization of Measuring Points for High-Speed...

Optimization of Measuring Points for High-Speed Motorized Spindle Thermal Error

Abstract:

According to the location and number of temperature measuring points of the motorized spindle thermal error, a new method for optimizing the locations of thermal key points is proposed. Firstly, temperature measuring points are divided into groups by using fuzzy clustering method. Secondly, grey correlation model is adopted to analyze emphasis of each measuring point to thermal deformation in temperature field distribution of motorized spindle. Finally, temperature measuring points have been optimally selected based on modified coefficient of determination. Comparing to the conclusion of the existed literature, the results show that this method is feasibility and validity. The method can reduce the temperature variables and modeling time, and supply the theoretic support for the engineering experience.

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Periodical:

Advanced Materials Research (Volumes 538-541)

Pages:

2113-2116

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.538-541.2113

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Online since:

June 2012

Authors:

Wei Dong Gou, Xin Wei Ye, Chun Li Lei, Zhi Yuan Rui

Keywords:

Fuzzy Clustering, Grey Degree of Correlation, Motorized Spindle, Optimization

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References

[1] D.A. Krulewich. Temperature integration model and measurement point selection for thermally induced machine tool errors [J]. Mechatronics, 1998, 8:395-412

DOI: 10.1016/s0957-4158(97)00059-7

Google Scholar

[2] L. A. Zadeh. Fuzzy sets. Information and Control, 1965, 8(3):338-353

Google Scholar

[3] C. Windischberger, M. Barth, C. Lamm, et al. Fuzzy cluster analysis of high-field functional MRI data[J]. Artificial Intelligence in Medicine, 2003, 29:203-223

DOI: 10.1016/s0933-3657(02)00072-6

Google Scholar

[4] Y. X.Luo, L. T. Zhang, M. Li, et al. Grey system theory and application in the mechanical engineering. National University of Defense Technology Press, Changsha, China, 2001, 20-31

Google Scholar

[5] J. L.Deng. Basis of grey theory. Huazhong University of Science and Technology Press, Wuhan, China, 2002, 53-68

Google Scholar

[6] J. S. Chen, W. Y. Hsu. Characterizations and models for the thermal growth of a motorized high speed spindle [J]. International Journal of Machine Tools and Manufacture, 2003(43):1163-1170

DOI: 10.1016/s0890-6955(03)00103-2

Google Scholar