Thermal Error Modeling of Numerical Control Machine Tools Based on Neural Network Neural Network by Optimized SSO Algorithm

doi:10.12068/j.issn.1005-3026.2021.11.008

Abstract

Abstract: In order to explore the complex thermal characteristics of five-axis NC(numerical control) machine tools， a method for thermal error modeling of cradle five-axis NC machine tools was proposed. The principle of shark smell optimization(SSO)algorithm and neural network composite modeling was adopted， which effectively improved the accuracy and modeling efficiency of the machine tool thermal error prediction model. Firstly， the temperature sensitive point was screened by using the thermal imager， and then the temperature sensor was placed at the position of the heat sensitive point of the machine tool. The collected thermal characteristic data were modeled by the above method. The results showed that the method is better than ABC neural network and PSO neural network in terms of modeling speed and accuracy. Finally， the model was applied to the thermal error compensation experiment of the five-axis machine tool， which improves its accuracy by 32%.

Key words: five-axis NC(numerical control) machine tool; shark smell optimization(SSO)algorithm; thermal error modeling; thermal error compensation; temperature key point

CLC Number:

TH117

HUANG Zhi， LIU Yong-chao， LIAO Rong-jie， CAO Xu-jun. Thermal Error Modeling of Numerical Control Machine Tools Based on Neural Network Neural Network by Optimized SSO Algorithm[J]. Journal of Northeastern University(Natural Science), 2021, 42(11): 1569-1578.

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