机床与液压
機床與液壓
궤상여액압
MACHINE TOOL & HYDRAULICS
2014年
7期
70-74,8
,共6页
表面粗糙度%能量消耗%多工序车削优化%NSGA-II算法%MOPSO算法
錶麵粗糙度%能量消耗%多工序車削優化%NSGA-II算法%MOPSO算法
표면조조도%능량소모%다공서차삭우화%NSGA-II산법%MOPSO산법
Surface roughness%Energy consumption%Multi-pass turning optimization%NSGA-II Algorithm%MOPSO Algorithm
在实际加工约束条件下，建立以表面粗糙度和能量消耗为目标的多工序车削优化模型的切削参数优化选择十分必要。运用NSGA-II算法和 MOPSO 算法对多工序车削模型进行优化比较。优化实例表明：NSGA-II 算法能够获得了比MOPSO算法更优的表面粗糙度、能量消耗的Pareto最优解集以及相应的粗、精切削参数，为多工序车削参数优化选择提供了依据。
在實際加工約束條件下，建立以錶麵粗糙度和能量消耗為目標的多工序車削優化模型的切削參數優化選擇十分必要。運用NSGA-II算法和 MOPSO 算法對多工序車削模型進行優化比較。優化實例錶明：NSGA-II 算法能夠穫得瞭比MOPSO算法更優的錶麵粗糙度、能量消耗的Pareto最優解集以及相應的粗、精切削參數，為多工序車削參數優化選擇提供瞭依據。
재실제가공약속조건하，건립이표면조조도화능량소모위목표적다공서차삭우화모형적절삭삼수우화선택십분필요。운용NSGA-II산법화 MOPSO 산법대다공서차삭모형진행우화비교。우화실례표명：NSGA-II 산법능구획득료비MOPSO산법경우적표면조조도、능량소모적Pareto최우해집이급상응적조、정절삭삼수，위다공서차삭삼수우화선택제공료의거。
Under condition of practical turning constraints,a bi-objective multi-pass turning optimization model,based on sur-face roughness and energy consumption,was very necessary for the optimization of machining parameters.The Non-dominated Sorting Genetic Algorithm-II (NSGA-II)and the Multi-objective Particle Swarm Optimization (MOPSO)were applied to the multi-pass turning optimization model.Example of optimization shows that the Pareto-optimal solutions set for surface roughness and energy consumption, and the corresponding machining parameters both precise and rough obtained by the NSGA-II Algorithm are more excellent than exam-ple results of MOPSO,which provides practical guides for selection optimization of machining parameters in multi-pass NC turning.
