Optimization of Process Parameters on Surface Hardness and Energy Consumption in Milling of 7050 Aluminum Alloy using Enhanced NSGA-II

Abstract Aluminum alloy has high strength and light weight. It is widely used for aircraft fuselage, propellers and other parts which work under high load conditions. High-quality parts made of aluminum alloy processed by computerized numerical control (CNC) machine often have the characteristics of high cost in their processing. In order to achieve high surface quality and control processing costs, this article takes the workpiece surface hardness and machining energy consumption as targets. Intelligent optimization algorithm is used to find the optimal combination of milling parameters to obtain ideal targets. CNC milling parameter optimization is a multi-parameter, multi-objective, multi-constraint, discrete nonlinear optimization problem which is difficult to solve. For this challenge, an improved NSGA-II is presented, named enhanced population diversity NSGA-II (EPD-NSGA-II). EPD-NSGA-II is improved with the normal distribution crossover, adaptive mutation operator of differential evolution, crowding calculation method considering variance and modified elite retention strategy to achieve enhanced population diversity. 12 test functions are chosen for experimentation to verify the performance of the EPD-NSGA-II. The values of three evaluation indicators show that the proposed approach has good distribution and convergence performance. Finally, the approach is applied in the milling parameters optimization of 7050 aluminum alloy to get the optimal solutions. Results indicate that the EPD-NSGA-II is effective in optimizing the problem of milling parameters.