基于改进遗传算法的模具零件孔群加工路径优化
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摘要
为提高孔群模具的加工效率,提出了一种最近邻、遗传算法和禁忌搜索相融合的改进遗传算法。采用最近邻算法选取一系列好的初始种群,同时将禁忌搜索中"禁忌"的思想引入到遗传算法中,并在进化过程中随机引入部分新个体,进行迭代搜索。根据孔群加工特点建立了类似旅行商问题的数学模型,并用改进算法求解最短加工路径,在分布复杂的孔类模具上进行数值实验。轮胎实例应用结果表明,改进算法优化后路径长度比CAM系统算法优化后路径长度缩短5.31%,比X向路径法缩短77.88%,比Y向路径法缩短77.63%,比最近邻算法缩短4.52%;当实验参数相同时,改进算法路径长度比遗传算法缩短14.65%,且运行时间平均缩短了63.60%。改进算法的路径长度明显缩短,有效提高了孔群的数控加工效率。其通用性较好,在提升数控系统孔群加工效率方面具有参考价值。
In order to improve the drilling efficiency of hole group for mold part, an improved genetic algorithm combining nearest neighbor, genetic algorithm and taboo search is proposed. The nearest neighbor algorithm is adopted to select a series of good initial populations, simultaneously the taboo search is introduced into the genetic algorithm, and some new individuals are randomly introduced in the evolution process to search optimal solution. Based on the drilling characteristics of the hole group, a mathematical model similar to the tsp is established and the improved algorithm is used to solve the drilling problem, and the experiments on the complex hole mold is carried out. The tire experiment results show that the path length of the improved algorithm is 5.31% shorter than that of the CAM system algorithm, 77.88% shorter than the X-path method's, 77.63% shorter than the Y-path method's, and 4.52% shorter than the nearest neighbor algorithm's. When the parameters are the same, the path length of the improved algorithm is 14.65% shorter than that of the genetic algorithm, and the running time is 63.60% shorter, showing that the improved algorithm can effectively improve the NC drilling efficiency of the hole group. The path length of the hole group for mold part based on the improved genetic algorithm is obviously shorter, and the algorithm has good versatility, providing some reference in improving the drilling efficiency of hole group with numerical control system.
In order to improve the drilling efficiency of hole group for mold part, an improved genetic algorithm combining nearest neighbor, genetic algorithm and taboo search is proposed. The nearest neighbor algorithm is adopted to select a series of good initial populations, simultaneously the taboo search is introduced into the genetic algorithm, and some new individuals are randomly introduced in the evolution process to search optimal solution. Based on the drilling characteristics of the hole group, a mathematical model similar to the tsp is established and the improved algorithm is used to solve the drilling problem, and the experiments on the complex hole mold is carried out. The tire experiment results show that the path length of the improved algorithm is 5.31% shorter than that of the CAM system algorithm, 77.88% shorter than the X-path method's, 77.63% shorter than the Y-path method's, and 4.52% shorter than the nearest neighbor algorithm's. When the parameters are the same, the path length of the improved algorithm is 14.65% shorter than that of the genetic algorithm, and the running time is 63.60% shorter, showing that the improved algorithm can effectively improve the NC drilling efficiency of the hole group. The path length of the hole group for mold part based on the improved genetic algorithm is obviously shorter, and the algorithm has good versatility, providing some reference in improving the drilling efficiency of hole group with numerical control system.
引文
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[11]王春香,郭晓妮.基于遗传蚁群混合算法的孔群加工路径优化[J].机床与液压,2011,39(21):43-45.WANG Chunxiang,GUO Xiaoni.Holes machining path optimization based on a hybrid algorithm integrated genetic algorithm with ant colony optimization[J].Machine Tool&Hydraulics,2011,39(21):43-45.
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[15]ISO 10303-21.Industrial automation systems-Product data representation and exchange-Part 21:Implementation method:Clear text encoding of the exchange structure[S].1997.
[2]龚玉玲,武美萍,徐晓栋,等.基于改进遗传算法的孔群数控加工路径优化[J].组合机床与自动化加工技术,2017(11):52-56.GONG Yuling,WU Meiping,XU Xiaodong,et al.Research on path optimization of hole group machining based on improved genetic algorithm[J].Modular Machine Tool&Automatic Manufacturing Technique,2017(11):52-56.
[3]段振云,孔令斌,赵文辉,等.覆盖件模具数控加工方法的研究[J].组合机床与自动化加工技术,2015(4):123-125.DUAN Zhenyun,KONG Lingbin,ZHAO Wenhui,et al.Research on the methods of covering parts mould NCmachining[J].Modular Machine Tool&Automatic Manufacturing Technique,2015(4):123-125.
[4]周正武,丁同梅.基于TSP和GA孔群加工路径优化问题的研究[J].组合机床与自动化加工技术,2007(7):30-32.ZHOU Zhengwu,DING Tongmei.Research on holes machining path planning optimization with TSP and GA[J].Modular Machine Tool&Automatic Manufacturing Technique,2007(7):30-32.
[5]ZHANG Weibo,ZHU Guangyu.Drilling path optimization by optimal foraging algorithm[J].IEEE Transactions on Industrial Informatics,2017(99):1-1.10.1109/TII.2017.2772314.
[6]文永军.旅行商问题的两种智能算法[D].西安:西安电子科技大学,2010.WEN Yongjun.Two kinds of Intelligent Algorithms for Traveling Salesman Problem[D].Xi′an:Xidian University,2010.
[7]汪定伟.智能优化方法[M].北京:高等教育出版社,2007.
[8]潘晓萌,李冰.蚁群算法优化和路径规划问题的应用研究[J].科技通报,2016,32(6):99-103.PAN Xiaomeng,LI Bing.Application of ant colony optimization and path planning[J].Bulletin of Science and Technology,2016,32(6):99-103.
[9]王磊,肖人彬.基于免疫记忆的人工免疫算法模型及其应用[J].模式识别与人工智能,2002,15(4):385-391.WANG Lei,XIAO Renbin.An algorithmic model of artificial immune system based on immunological memory and its implementation[J].Pattern Recognition and Artificial Intelligence,2002,15(4):385-391.
[10]肖军民.一种改进遗传算法在孔群加工路径中的优化[J].组合机床与自动化加工技术,2015(2):151-153.XIAO Junmin.Optimization of NC machining path for holes based on improved genetic algorithm[J].Modular Machine Tool&Automatic Manufacturing Technique,2015(2):151-153.
[11]王春香,郭晓妮.基于遗传蚁群混合算法的孔群加工路径优化[J].机床与液压,2011,39(21):43-45.WANG Chunxiang,GUO Xiaoni.Holes machining path optimization based on a hybrid algorithm integrated genetic algorithm with ant colony optimization[J].Machine Tool&Hydraulics,2011,39(21):43-45.
[12]赵继俊.优化技术与MATLAB优化工具箱[M].北京:机械工业出版社,2011.
[13]SUH S H,CHO J H,HONG H D.On the architecture of intelligent STEP-compliant CNC[J].International Journal of Computer Integrated Manufacturing,2002,15(2):168-177.
[14]盛滨.基于STEP-NC的信息提取系统的研究与实现[D].厦门:厦门大学,2008.SHENG Bin.Research and Implementation of Information Extraction System based on STEP-NC[D].Xiamen:Xiamen University,2008.
[15]ISO 10303-21.Industrial automation systems-Product data representation and exchange-Part 21:Implementation method:Clear text encoding of the exchange structure[S].1997.