Research on Optimization of CNC Milling Parameters Based on GA-BP and NSGA-Ⅱ
Efficient and low-carbon manufacturing is the key to exploring sustainable development.As a common cold machining method for metal surfaces,CNC milling often has the problems of short tool life and high carbon emissions.This paper proposes a GA-BP multi-objective optimization method based on NSGA-Ⅱ.Based on the data sets of CNC milling tool life and carbon emission under different machining parameters,the GA-BP neural network tool life and carbon emission pre-diction model is established.Based on the NSGA-Ⅱ algorithm,the main optimization model with tool life and carbon emis-sion as the goal is established,and the constructed GA-BP neural network model is called as the objective function to opti-mize and solve the Pareto optimal solution set.The TOPSIS optimal solution decision is made for the Pareto optimal solution set,and the combination of machining parameters for comprehensive optimization of tool life and carbon emissions is ob-tained.The optimization results show that this method can not only accurately predict the life and carbon emissions of CNC milling tools,but also effectively optimize them,which has certain theoretical guiding significance for the optimization of CNC milling parameters.
万方数据
维普
