Optimization of CNC Milling Parameters Based on Tool Life and Process Carbon Emission Prediction Models

Abstract

Abstract Aiming at the problems of short tool life and high carbon emission in the process of CNC milling, a multi-objective optimization method of process parameters based on the BP neural network and NSGA-II algorithm is proposed. Based on the data sets generated by CNC milling under different process parameters, with tool life and process carbon emissions as outputs and different process parameter combinations as inputs, a BP neural network-based tool life and process carbon emissions prediction model is constructed. Taking the maximum tool life and minimum process carbon emission as the optimization goals, the NSGA-II process parameter multi-objective optimization main model is constructed. The model is then used as the objective function of the main model and optimized to obtain the Pareto optimal solution set. The optimal solution decision is made on the Pareto optimal solution set using the Topsis method, and the optimal process parameter combination is obtained. The optimization results show that the method can accurately predict the tool life and process carbon emission of CNC milling. It can also effectively optimize the process parameters, which provides a new method for the low-carbon optimization of CNC milling parameters.