Global optimization of a feature-based process sequence using GA and ANN techniques
Global optimization of a feature-based process sequence using GA and ANN techniques
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DOI：
10.1080/00207540500137292
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发表时间：
2005-08-01
影响因子：
9.2
通讯作者：
Parkin, R
中科院分区：
文献类型：
--
作者：
Ding, L;Yue, Y;Parkin, R
工序排序一直是计算机辅助工艺设计（CAPP）研究和开发的一个重要领域。一个最佳的工艺顺序可以大大提高生产效率，降低生产成本。遗传算法（GA）是一种通过适者生存来寻求“培育”复杂问题的良好解决方案的技术。一些使用遗传算法的尝试已经在操作排序优化上进行了，但是很少有系统打算提供全局优化的最佳函数定义。此外，大多数系统缺乏适应性或无法学习。提出了一种基于多目标适应度的工艺排序优化策略：最小制造成本、最短制造时间和最佳满足顺序规则。提出了一种将遗传算法、神经网络和层次分析法相结合的工艺排序方法。在简要分析了工艺设计的研究现状后，对工艺设计的相关问题进行了阐述.然后定义了一个全局优化的适应度函数，包括使用层次分析法评估制造规则，使用神经网络技术计算成本和时间以及确定相对权重。提出了基于遗传算法的工艺排序，实施和测试结果进行了讨论。最后，对全文进行了总结，并提出了今后的工作方向。
Operation sequencing has been a key area of research and development for computer-aided process planning (CAPP). An optimal process sequence could largely increase the efficiency and decrease the cost of production. Genetic algorithms (GAs) are a technique for seeking to `breed' good solutions to complex problems by survival of the fittest. Some attempts using GAs have been made on operation sequencing optimization, but few systems have intended to provide a globally optimized fittest function definition. In addition, most of the systems have a lack of adaptability or have an inability to learn. This paper presents an optimization strategy for process sequencing based on multi-objective fittness:minimum manufacturing cost, shortest manufacturing time and best satisfaction of manufacturing sequence rules. A hybrid approach is proposed to incorporate a genetic algorithm, neural network and analytical hierarchical process (AHP) for process sequencing. After a brief study of the current research, relevant issues of process planning are described. A globally optimized fittness function is then defined including the evaluation of manufacturing rules using AHP, calculation of cost and time and determination of relative weights using neural network techniques. The proposed GA-based process sequencing, the implementation and test results are discussed. Finally, conclusions and future work are summarized.