Multivariate Correlative and Combined Thermal Error Model for the CNC Machine Tool with Experimental Validation

doi:10.12068/j.issn.1005-3026.2017.05.019

Abstract

Abstract:

To solve the problem that based on machine tool temperature that is comprehensively influenced by other factors， and thus the thermal error models had poor robustness， a multivariate correlative and combined thermal error model was put forward， which overall considers machine tool temperature， speed of power source， and temperature of coolant and environment. Least squares support vector (LS-SVM) method was applied to the thermal error model， and partial least squares (PLS) method was applied to extract the principal components as the input of LS-SVM， and the PLS-LSSVM thermal error combined model was then formulated. In addition， this model set the differential temperatures， relatively with initial temperatures， as the temperature variable， which is based on the process of numerical control machining and the principle of material thermal deformation， to make the thermal error compensation more accurate. It was tested on a precision machining center， whose results showed that the PLS-LSSVM thermal error model is more stable than the LS-SVM model， and more accurate than the partial least squares regression (PLSR) model. Besides， the root mean square error (RMSE) of the predictive thermal error with the PLS-LSSVM model is 5.5μm on average less than that with the PLS-LSSVM* model， which only takes into account the temperature measurements of the machine tool.

Key words: CNC machine tool, thermal error model, influence factor of thermal error, partial least squares, least squares support vector machine

CLC Number:

TH161.4

MA Yue， WANG Hong-fu， SUN Wei， HUANG Yu-bin. Multivariate Correlative and Combined Thermal Error Model for the CNC Machine Tool with Experimental Validation[J]. Journal of Northeastern University:Natural Science, 2017, 38(5): 700-705.

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