Research On Thermal Error Modeling Algorithm Of Spindle Of CNC Lathe

With the improvement of the technical level of industrial products,as well as the higher and higher requirements for the accuracy of functional components,the machining accuracy of machine tools needs to be further improved.Among the many error sources that affect the machining accuracy of machine tools,thermal error is the biggest factor that affects the machining accuracy of machine tools.Therefore,using thermal error compensation technology to compensate thermal error is an economic and effective measure to improve the machining accuracy of machine tools.A large number of domestic and foreign research data show that the thermal error compensation technology focuses on the selection of temperature sensitive measuring points,thermal error modeling and compensation.The key of thermal error compensation technology is to build a thermal error model with high prediction accuracy and strong robustness.Aiming at the key problems of thermal error compensation technology,this paper takes a CNC lathe spindle as the research object,combines the actual measured temperature and thermal error data,explores the method of temperature sensitive point screening and establishes an effective thermal error model,The purpose of this paper is to provide a thermal error modeling method with strong robustness and high prediction accuracy.The error modeling method with strong robustness and high prediction accuracy is given to improve the accuracy and stability of the thermal error compensation technology.The main research contents of the paper are as follows:A fusion algorithm model is proposed to predict the thermal error of lathe spindle.K-means++clustering algorithm and Pearson correlation coefficient are used to determine the key temperature measurement points,and the evaluation index of clustering results is given.Due to the wavelet neural network is sensitive to the initial value,the bat algorithm is used to optimize the initial connection coefficient of the wavelet neural network,and the prediction model of the thermal error of the main axis of the wavelet neural network based on the bat algorithm is established.Due to the initial clustering center will affect the clustering effect of K-means clustering algorithm,in this paper,AP clustering algorithm is used to determine the initial clustering center of K-means clustering algorithm.In order to improve the prediction ability of the thermal error model,the generalized regression neural network is used to predict the axial thermal error of the lathe spindle.However,the smooth factor affects the prediction ability of the generalized regression neural network,so a reasonable value of the smooth factor should be obtained to ensure good prediction ability.Genetic algorithm is used to determine the smoothing factor of the generalized regression neural network,and a prediction model of spindle thermal error based on GA-GRNN neural network is established.A thermal error model of lathe spindle based on grey neural network optimized by Drosophila algorithm is proposed.Because the prediction ability of the gray neural network is affected by the initial coefficient.To solve this problem,the initial coefficients of fruit fly algorithm were optimized,and the optimized gray neural network was used to establish the thermal error model.Finally,the thermal error model of spindle based on grey neural network optimized by Drosophila algorithm is established.The modeling experiments verify that the fruit fly algorithm can optimize the initial parameters of the gray neural network.In addition,the optimization of the gray neural network based on the fruit fly algorithm is capable of modeling thermal error.