In the machining process of CNC grinding machine, the non-uniformity of heat source often leads to the deformation of the workpiece, the intensification of wear and the machining error, so the optimization of thermal energy environment has become an important research direction to improve the machining performance. The aim of this study is to optimize the thermal energy environment of CNC grinding machine by improving the neural network algorithm, so as to achieve the goal of improving the machining precision and surface quality. We hope that by establishing thermal environment model, we can deeply analyze the characteristics of thermal energy distribution and put forward effective optimization measures. The improved neural network model is used to model the heat energy distribution of CNC grinding machine. The accuracy of the model is improved by introducing multi-layer perceptron and convolutional neural network. At the same time, combined with the experimental data, training and testing are carried out to verify the effectiveness of the model. Genetic algorithm and fuzzy control are used to adjust the thermal energy environment and obtain the best process parameters. The experimental results show that the prediction accuracy of the improved neural network model is significantly improved, and the prediction error of the model is significantly reduced compared with the traditional method. After thermal optimization, the surface roughness of the workpiece is reduced, the dimensional accuracy is significantly improved, and the overall processing efficiency is significantly improved. By improving the neural network model, the thermal energy environment of CNC grinding machine is effectively optimized, and the machining precision and surface quality are successfully improved.
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