摘要
在实际数控生产加工过程中,切削参数的优化对于保证加工质量、提高生产效率和降低加工成本具有非常重要的意义。为计算以单位生产成本最小为优化目标的多工序车削非线性优化模型,在NSGA-II算法基础上提出了一种新的自适应搜索非支配排序遗传算法(ASNSGA)。多工序车削加工实例结果表明,与模拟退火算法(SA/PA)、分散搜索算法(SS)及浮点编码遗传算法(FEGA)优化算法比较,自适应搜索非支配排序遗传算法得到最低的单位生产成本,有助于数控加工中粗车进给量、粗车切削速度及精车进给量、精车切削速度等切削参数的优化选择。
The optimization of cutting parameters is significant for machining quality,production efficiency and machining economics in practical NC machining process.In order to calculate the optimum goal for minimum unit production cost,an adaptive search non-dominated sorting genetic algorithm（ASNSGA） is applied to multi-pass turning operations nonlinear cutting optimization model subject to various practical cutting constraints,which is based on non-dominated sorting genetic algorithm-II.By comparing with those of genetic algorithms（GA）,simulated annealing algorithm（SA/PA）,scatter search（SS）,float encoded genetic algorithm（FEGA）,the cutting optimization model experimental results obtained by the proposed multi-pass turning operations nonlinear cutting optimization algorithms,named ASNSGA-II,are effective for solving complex nonlinear cutting optimization problem,reducing the unit production cost,and helping for the cutting parameters optimum selection such as feed rate and cutting rate in rough NC machining,feed rate and cutting rate in finish NC machining.
出处
《机械设计与制造》
北大核心
2013年第7期119-122,共4页
Machinery Design & Manufacture
基金
湖北省武汉市属高校科研项目(2010140)
关键词
单位生产成本
自适应搜索非支配排序遗传算法
多工序车削切削参数优化
粗精车进给量
粗精车切削速度
Unit Production Cost
Adaptive Search Non-Dominated Sorting Genetic Algorithm
Multi-Pass Turning Operations
Optimization of Cutting Parameters
Feed Rate in Rough and Finish Machining
Cutting Speed in Rough and Finish Machining
