thermal error modeling of the cnc machine tool based on data fusion method of kalman filter

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2017
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Abstract
This paper presents a modeling methodology for the thermal error of machine tool. The temperatures predicted by modified lumped-mass method and the temperatures measured by sensors are fused by the data fusion method of Kalman filter. The fused temperatures, instead of the measured temperatures used in traditional methods, are applied to predict the thermal error. The genetic algorithm is implemented to optimize the parameters in modified lumped-mass method and the covariances in Kalman filter. The simulations indicate that the proposed method performs much better compared with the traditional method of MRA, in terms of prediction accuracy and robustness under a variety of operating conditions. A compensation system is developed based on the controlling system of Siemens 840D. Validated by the compensation experiment, the thermal error after compensation has been reduced dramatically.
Reference Key
wang2017mathematicalthermal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Haitong Wang;Tiemin Li;Yonglin Cai;Heng Wang
Journal journal of power sources
Year 2017
DOI
10.1155/2017/3847049
URL
http://dx.doi.org/10.1155/2017/3847049
Keywords
civil engineering (general)mathematics engineering (general)

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