面向铝合金加工的GRA-SVM-CPSO混合多目标优化方法

Multi-objective Parameter Optimization of Aluminum Alloy Machining via GRA-SVM-CPSO Hybrid Method

为有效改善铝合金切削时不同指标优化出现的冲突问题,本文提出了一种新的多目标优化方法。首先,应用灰色关联分析(Grey relations analysis,GRA)将切削加工过程中切削力、表面粗糙度和材料去除率等多目标问题转化为灰色关联度(Grey relational grade,GRG)的单目标问题;然后,基于支持向量机模型(Support vector machine,SVM)建立切削参数与GRG之间关联模型;最后,以切削力和表面粗糙度最小化、材料去除率最大化为优化目标,采用混沌粒子群优化算法(Chaos particle swarm optimization, CPSO)优选得到的铝合金加工最优参数(切速为400 m/min,进给为0.15 mm/r,切深为1 mm)。将优化结果与粒子群算法(Particle swarm optimization,PSO)对比发现,CPSO算法具有更强的全局搜索能力,能够更快地收敛至全局最佳位置,获得更好的优化结果。

In order to improve the conflict problem of different index optimization in aluminum alloy machining effectively, a new multi-objective optimization method has been proposed in this paper. Firstly, grey relations analysis(GRA) has been applied to convert the multi-objective problems of cutting force, surface roughness and material removal rate in the machining into singleobjective problem of grey relational grade(GRG). Then, a correlation model between the cutting parameters and GRG has been constructedvia support vector machine(SVM). Finally, the chaos particle swarm optimization(CPSO) has been applied to find the optimal processing parameters for minimizing cutting force and surface roughness and maximizing material removal rate at a cutting speed of 400 m/min, feed rate of 0.15 mm/r and cutting depth of 1 mm in the turning of aluminum alloy and it turns out that the CPSO algorithm has better global search capabilities,can converge to the global optimal position faster and obtain better optimization results comparing with particle swarm optimization(PSO) algorithm.