摘要
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。
Under condition of practical turning constraints, a bi-objective muhi-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.
出处
《机床与液压》
北大核心
2014年第7期70-74,8,共6页
Machine Tool & Hydraulics
