Research On Thermal Error Prediction Model Of CNC Gear Shaper Based On Artificial Intelligence Algorithm

With the advancement of science and technology and the rapid development of industrial automation,the machining accuracy of machine tools has become the focus of attention and research.A large number of studies have shown that the thermal error of CNC machine tools is the main reason that affects the machining accuracy,accounting for 40%-70% of the overall error of the machine tool.Therefore,realizing the thermal error control of CNC machine tools is one of the important ways to improve the machining accuracy of CNC machine tools.At present,mainly through the establishment of thermal error model,the unmeasured thermal error of the machine tool is compensated to improve the running accuracy of the machine tool.In this paper,the thermal error compensation method is used to study the precision improvement of the CNC gear shaper.Gears are mechanical parts used to transmit motion and power,and are widely used in industrial production.The manufacturing level of gears has become one of the symbols of a country's machinery manufacturing level.Therefore,it is of great practical significance to study the relevant machine tools for processing gears.The CNC gear shaper is the main processing equipment for gears,so the research on the thermal error of the CNC gear shaper is of great significance.In order to reveal the thermal deformation law of the NC gear shaper,the thermal error law test and modeling prediction of the thermal deformation generated in the X and Y directions of the main shaft are given.At present,there are many modeling algorithms,and neural network algorithms have been rapidly popularized in various fields of the world because of their accurate prediction accuracy and flexibility.Therefore,the neural network algorithm is used to model the thermal error of the gear shaper for the first time in this paper.However,there are various neural network algorithms,and through research,it is found that there are differences in the compensation effects of different algorithms.Therefore,in this paper,three common and representative neural network algorithms are selected to study the thermal error characteristics of CNC gear shapers,and the accuracy is compared with the widely used multiple linear regression algorithm.In this paper,the YKS5132DX3 CNC gear shaper is used as the experimental object,and the temperature of the sensitive point and the thermal error value of the XY direction of the spindle are obtained.GA-BP)and particle swarm optimization neural network(PSO-BP)thermal error prediction model.Through the comparison of the prediction effects of these four algorithms,the differences in the compensation effects of different algorithms are analyzed to select the optimal algorithm.The results show that the particle swarm optimization BP neural network algorithm model has high prediction accuracy and strong robustness,which provides a practical reference for the selection of thermal error compensation models for CNC machine tools,and has good engineering applicability.