Optimization of computer numerical control machining process parameters for mechanical equipment components based on improved GA

Abstract

Computer numerical control machining process parameter optimization for mechanical equipment components is a key link in improving machining efficiency and quality. It can not only significantly reduce production costs, but also ensure the accuracy and reliability of the components. To achieve process parameter optimization, orthogonal experimental method is adopted and different combinations of process parameters are designed. To clarify the influence of different parameter combinations on the milling force and machining surface quality of components, the simulation is conducted. The study also constructs a multi-objective optimization model for process parameters and designs an improved genetic algorithm (GA) to solve. The improved GA introduces dynamic adaptive mechanism and gravity search algorithm to reduce the discreteness of the solution. The results showed that the average errors between the predicted and measured milling forces in the horizontal, longitudinal, and vertical directions were 2.31, 5.40, and 3.03, respectively. In unimodal, multi-modal, and mixed functions, the fitness values of the improved GA were superior to those of other comparison algorithms, and the standard deviation was smaller. After optimizing the process parameters, the average surface roughness of the workpiece decreased from 1.9800 to 1.3407, with a decrease rate of 32.29%, and the average residual stress decreased from 182.57 MPa to 127.82 MPa, with a decrease rate of 29.99%. The improved GA has stable performance and good optimization effect, which can provide technical support for process parameter optimization to improve workpiece quality.