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Abstract

To improve the nonlinear fitting ability of multiple linear regression model in thermal error prediction of machine tools, an optimized thermal error modeling method based on test and simulation is proposed in this paper. Firstly, the finite-element analysis is used to complete the thermal-structure coupling analysis of the CNC machine tool. Based on the simulation results of temperature and deformation field of the machine tool, temperature-sensitive points (TSPs) are selected with fuzzy cluster analysis and correlation analysis. According to the simulation results, TSPs of the machine tool are fitted by quadratic polynomial from temperature to deformation. Finally, taking the deformation value of TSPs as the intermediate variable, the multiple linear regression model is established, and the optimized quadratic multiple regression thermal error model is obtained. The results show that the prediction curve of the optimized thermal error model based on on test and simulation is closer to the test curve, and the fitting index is better than that obtained from the traditional model. This method can effectively improve the nonlinear fitting ability of the thermal error model of CNC machine tools. Based on the thermal error model established in this paper, the compensation value of thermal error can be obtained by inputting the real-time measured temperature data of TSPs, which can effectively improve the machining accuracy of CNC machine tools.

Keywords

CNC machine tools thermal structural coupling thermal error temperature-sensitive points thermal error model

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Research on optimal multivariate thermal error modeling based on finite-element analysis

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