Research on Data-Driven Optimization Algorithm for Machining Parameters of CNC Lathe

The purpose of this study is to optimize the machining parameters of CNC lathe by scientific and efficient methods and enhance the competitiveness of manufacturing industry. Firstly, the real-time monitoring data of CNC lathe machining process, including cutting speed, feed speed, cutting depth, etc., are collected, and the data are denoised by median filtering method and wavelet transform method to extract key features. Then, the mathematical mapping relationship between machining parameters and machining quality is established by using multiple linear regression model. On this basis, a GA-NN hybrid optimization algorithm combining genetic algorithm (GA) and neural network (NN) is designed to improve the efficiency and accuracy of machining parameter optimization. The simulation results show that the algorithm can significantly reduce the surface roughness, improve the dimensional accuracy and effectively improve the material removal rate (MRR). The research results show that the optimization algorithm of CNC lathe machining parameters based on data-driven has remarkable effect in improving machining quality and efficiency, which is especially important for high-precision parts manufacturing.