基于文化基因算法的装夹规划方法  

Setup Planning Method Based on Memetic Algorithm

作　　者：高博[1,2] 阎艳[2] 张发平[2] 王国新[2] 

机构地区：[1]兰州交通大学机电工程学院,兰州730070 [2]北京理工大学机械与车辆学院,北京100081

出　　处：《机械工程学报》2015年第3期162-169,共8页Journal of Mechanical Engineering

基　　金：国家自然科学基金(51375049);国家部委预先研究(513180102)资助项目

摘　　要：针对计算机辅助工艺规划中的装夹规划问题,提出一种基于Memetic算法的装夹规划方法。根据零件的几何特征,确定加工特征和最小加工单元,建立零件装夹规划的表示方法。为每个加工单元配置候选的刀具接近方向、机床和刀具等装夹特征,初始化装夹规划种群。通过部分匹配交叉操作和插入变异操作,在全局范围内搜索装夹规划方案。基于加工单元之间的顺序约束,通过二叉树调序算法将非可行解转化为可行解。将加工单元之间的装夹相似性之和作为适应度函数,以适应率为向导进行交叉操作,在非约束加工单元之间进行变异操作,在局部范围内搜索适应度值高的装夹方案。经过种群进化过程,获得最优或者较优的装夹规划方案。通过典型零件的装夹规划验证了该方法的可行性和有效性。To deal with setup planning in computer aided process planning, a novel setup planning method based on memetic algorithm is proposed. By analyzing geometric characteristics of the part, machining features and units are determined and representation of setup planning is established. The initialize population of setup planning is configured by candidate tool approach direction, machines and cutter for each machining unit. Setup planning is searched in the global scope by partial mapped crossover and insertion mutation. Based on sequence constraints between units, binary tree sort algorithm is adopted to transform from infeasible solution to feasible solution. The sum of the processing methods similarity between machining units is taken as fitness function, setup planning of high fitness value can be acquired in local search by crossover operation based on fitness rate and mutation operation of non-sequential constraint machining units. After the evolution of populations, optimal setup planning solution is generated. Setup planning process of typical part is illustrated to prove the feasibility of the proposed model.

关 键 词：装夹规划 文化基因算法 加工顺序约束 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]