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基于改进蚁群算法的最优加工工艺路线规划 被引量:2

Optimal process route planning based on improved ant colony algorithm
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摘要 定义了加工特征、加工链以及加工单元的基本概念。利用执行成本加权求和计算加工单元之间的广义欧式距离。通过对信息素浓度的更新由实时更新改进为循环后更新,使新的蚁群算法不仅简化了计算复杂性,而且增强了最优工艺路线规划的客观性。根据机加工之间具体的约束方式以及蚂蚁选择节点方式规定寻径过程中的tuba表。建立了基于改进蚁群算法的最优加工工艺路线规划算法流程。 The basic concepts of the processing characteristics, the processing chain, as well as the processing unit were defmed. Euclidean distance between the processing units was calculated based on the implementation of cost-weighted sum. Improvement of cycle was updated through the update of pheromone concenlration by real-time updates. The new ant colony algorithm not only simplifies the computational complexity, but also enhances the objectivity of the optimal process planning. According to the specific constraints between machining and the ants choose the node to the provisions of the tuba in the process of muting. Process of the optimal process route planning algorithm based on improved ant colony algorithm was established.
作者 燕金华
机构地区 东营职业学院
出处 《自动化与仪器仪表》 2012年第3期164-166,共3页 Automation & Instrumentation
关键词 工艺路线 蚁群算法 信息素 Process Route Ant Colony Algorithm Pheromone
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  • 1张映锋,江平宇,周光辉.基于遗传算法的e-制造调度系统研究[J].计算机集成制造系统,2004,10(8):955-961. 被引量:5
  • 2单宝峰,张继宇.对航空圆柱齿轮加工工艺过程的探讨[J].机械设计与制造,1995(4):34-37. 被引量:2
  • 3肖鹏,王冰.模糊神经网络技术在防摇控制系统中的研究与应用[J].起重运输机械,2005(11):21-23. 被引量:8
  • 4陈桦,王涛,孙波.基于人工神经网络的智能化CAPP系统应用[J].微计算机信息,2005,21(12Z):189-190. 被引量:3
  • 5Zhang Y F, Nee A Y C. Using genetic algorithm in processing planning for job shop machining [J]. IEEE Trans on Evolutionary Computation, 1997, (1): 278-289.
  • 6XUE Deyi. A multilevel optimization approach considering product realization process alternatives and parameters for improving manufacturability [J]. J Manufacturing System, 1997, 16(5): 338-351.
  • 7Vancza J, Markus A. Genetic algorithm in process planning [J]. Computers in Industry, 1991, 17: 181-194.
  • 8Kiritsis D, Porchet M. A genetic petri net model for dynamic process planning and sequence optimization [J]. Advances in Eng Software, 1996, 25(1): 61-71.
  • 9Rocha J, Ramos C, Vale Z. Process planning using a genetic algorithm approach [A]. Proc of the 1999 IEEE Int Symp on Assembly and Task Planning [C]. Porto, Portugal: IEEE, 1999. 82-86.
  • 10Rudolph G. Convergence analysis of canonical genetic algorithms [J]. IEEE Trans on Neural Networks, 1994, 5(1): 96-101.

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