Operation sequencing in CAPP using genetic algorithms
Operation sequencing in CAPP using genetic algorithms
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DOI：
10.1080/002075499191409
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发表时间：
1999-03-20
影响因子：
9.2
通讯作者：
Narendran, TT
中科院分区：
文献类型：
--
作者：
Reddy, SVB;Shunmugam, MS;Narendran, TT
计算机辅助工艺规划（CAPP）是计算机集成制造（CIM）中连接计算机辅助设计（CAD）和计算机辅助制造（CAM）的重要接口。工艺规划中的工序排序是指在满足零件图中规定的相关工艺约束条件下，按步骤选择加工工序，以生产零件的各个形状特征。在不断变化的生产环境中，单一的操作顺序可能不是最好的，因为生产环境具有多个目标，例如最小化设置数量，最大化机器利用率和最小化工具更换数量。本文演示了遗传算法作为一种全局搜索技术在动态规划环境中快速识别最优或近最优操作序列的应用。设计了一种新的初始化方案来表示遗传密码，并设计了一个新的交叉算子来保留每个形式特征的局部操作优先级。由于序列可以快速获得，因此该方法实际上可以被工艺规划人员用于为当前操作环境生成替代的可行序列。
Computer aided process planning (CAPP) is an important interface between computer aided design (CAD) and computer aided manufacturing (CAM) in computer integrated manufacturing (CIM). Operation sequencing in process planning is concerned with the selection of machining operations in steps that can produce each form feature of the part by satisfying relevant technological constraints specified in the part drawing. A single sequence of operations may not be the best for all the situations in a changing production environment with multiple objectives such as minimizing number of set-ups, maximizing machine utilization and minimizing number of tool changes. This paper demonstrates the application of genetic algorithms as a global search technique for a quick identification of optimal or near optimal operation sequences in a dynamic planning environment. A novel initialization scheme for representing the genetic code and a new crossover operator are designed to retain the local operation precedence for each form feature. Since sequences can be obtained quickly, this approach can actually be used by the process planner to generate alternative feasible sequences for the prevailing operating environment.