机床与液压
機床與液壓
궤상여액압
MACHINE TOOL & HYDRAULICS
2014年
4期
21-25
,共5页
表面粗糙度%切削能量%NSGA-II算法%MOPSO算法%节能%精车车削优化
錶麵粗糙度%切削能量%NSGA-II算法%MOPSO算法%節能%精車車削優化
표면조조도%절삭능량%NSGA-II산법%MOPSO산법%절능%정차차삭우화
Surface roughness%Cutting energy%NSGA-II algorithm%MOPSO algorithm%Energy saving%Optimization of turning in finish machining
考虑车削加工约束条件，建立切削能量最小与表面粗糙度最小的精车车削优化模型。通过实例运用非支配排序遗传算法（NSGA-II）与多目标粒子群算法（MOPSO）对精车优化切削模型进行仿真优化，结果表明NSGA-II 算法与MOPSO算法切削能量和表面粗糙度的 Pareto 最优解集均可由同一的六次曲线方程拟合，且拟合相关指数为0.9995、0.9982。在表面粗糙度和切削能量的Pareto最优解集下，获得了精车优化切削模型相应的进给量、切削速度，为优化选择精车切削参数提供了参考。
攷慮車削加工約束條件，建立切削能量最小與錶麵粗糙度最小的精車車削優化模型。通過實例運用非支配排序遺傳算法（NSGA-II）與多目標粒子群算法（MOPSO）對精車優化切削模型進行倣真優化，結果錶明NSGA-II 算法與MOPSO算法切削能量和錶麵粗糙度的 Pareto 最優解集均可由同一的六次麯線方程擬閤，且擬閤相關指數為0.9995、0.9982。在錶麵粗糙度和切削能量的Pareto最優解集下，穫得瞭精車優化切削模型相應的進給量、切削速度，為優化選擇精車切削參數提供瞭參攷。
고필차삭가공약속조건，건립절삭능량최소여표면조조도최소적정차차삭우화모형。통과실례운용비지배배서유전산법（NSGA-II）여다목표입자군산법（MOPSO）대정차우화절삭모형진행방진우화，결과표명NSGA-II 산법여MOPSO산법절삭능량화표면조조도적 Pareto 최우해집균가유동일적륙차곡선방정의합，차의합상관지수위0.9995、0.9982。재표면조조도화절삭능량적Pareto최우해집하，획득료정차우화절삭모형상응적진급량、절삭속도，위우화선택정차절삭삼수제공료삼고。
In consideration of various practical constraints,the finish turning operations cutting model,based on the minimum cutting energy and the minimum surface roughness,was proposed.The non-dominated sorting genetic algorithm-II (NSGA-II)and the multi-objective particle swarm optimization (MOPSO)were applied in examples to optimize finish turning cutting model.The results indicate that the Pareto-optimal solutions set of cutting energy and surface roughness by using NSGA-II algorithm and the MOPSO algo-rithm can all be fitted by the same 6-th polynomials of degree equation of curves,and the fitting relevant index is equal to 0.999 5 and 0.998 2.Under the Pareto-optimal solutions set for the surface roughness and the cutting energy,feed rates and cutting speed can be also obtained for the cutting model,which provides practical references for optimal selection of finish machining parameters.
