Multiobjective Optimization of Milling Parameters for Ultrahigh‐Strength Steel AF1410 Based on the NSGA‐II Method

Abstract

In this paper, ultrahigh‐strength steel AF1410 was milled with the carbide tool, and a total of thirty experiments were performed based on central composite design (CCD) of response surface methodology. The prediction models of milling force and surface roughness are established, respectively. The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA‐II) with milling force, surface roughness, and material removal rate as optimization objectives. The surface roughness, cutting force, and material removal rate are important indexes to measure the energy consumed in the process of product, the surface machining quality, and machining efficiency of processing, respectively. In order to minimize milling force and surface roughness and maximize material removal rate, NSGA‐II was used for multiobjective optimization to obtain the optimal fitness value of the objective function. The NSGA‐II has been applied to obtain a set of optimal combination of parameters from the Pareto‐optimal solution set to enhance the machining conditions.