非支配排序最优保留遗传算法的低成本车削

Low Cost Turning of Optimum Remains Non-dominated Sorting Genetic Algorithm (ORNSGA)

切削参数优化对于加工质量、生产效率、加工成本、利润具有非常重要的意义。提出一种新算法即非支配排序最优保留遗传算法(ORNSGA),并应用于有约束条件的多工序车削模型优化。加工实例结果表明:与混合人工蜂群算法(HABC)、差分进化受体编辑算法(DERE)、粒子群受体编辑算法(PSRE)、混合搜索算法(HTHS)、混合鲁棒遗传算法(HRGA)及模拟退火算法(SA/PA)比较,用非支配排序最优保留遗传算法得到了最低的单位生产成本,不仅节约了生产成本,而且很好地解决了切削参数优化问题,如数控车削中的粗车进给量、粗车切削速度及精车进给量、精车切削速度。

The optimization of cutting parameters is very important for machining quality,production efficiency,machining eco-nomics and profit. A new optimization approach was proposed,named optimum remains non-dominated sorting genetic algorithm （ORNSGA）,and was applied to the multi-pass turning optimization model subject to various practical constraints. By compared with those of hybrid artificial bee colony algorithms （HABC）,differential evolution receptor editing algorithm （DERE）,particle swarm re-ceptor editing algorithm （PSRE）,hybrid taguchi-harmony search algorithm （HTHS）,hybrid robust genetic algorithm （HRGA）and simulated annealing algorithm （SA/PA）,the minimum unit production cost was obtained by using the presented algorithms （ORNS-GA）. Not only the unit production cost is saved,but also cutting optimization problem,such as feed rate and cutting rate in rough NC machining,feed rate and cutting rate in finish NC machining,are effectively solved.