锻压技术

多变量编码遗传算法在管道类零件展开图排样中的应用

英文标题：Application of multivariate coding genetic algorithm in expanded layout for ventilation duct parts

作者：张京京龙华陈晓鹏陈年华

分类号：TP3-05

出版年,卷(期):页码：2022,47(11):146-150

摘要：

针对不规则通风管道展开件在排样中存在的材料利用率低、排样效率低、展开件形状难以表征等问题，在基本遗传算法的基础上提出了采用多变量编码遗传算法对展开件进行排样布局。该算法对零件排列次序、镜像参数、旋转角度和零件中心位置存储参数等多个变量采用十进制编码方法，随机生成初始种群，进行选择、交叉、变异操作，最终获得了展开件排样效果图。对比基本遗传算法与多变量编码遗传算法的排样效果可知，多变量编码遗传算法能够准确地表征排样件的几何形状及位置特征，保证了高效率排样，提高了排样的材料利用率，同时，验证了多变量编码遗传算法具有可行性与有效性，对于优化钣金件制造加工工艺、提高生产效率具有重要指导意义。

For the problems of low utilization rate of material, low efficiency of layout and difficult to characterize the shape of expanded part in the expanded layout of irregular ventilation duct parts, the layout of expanded part was proposed by a multivariate coding genetic algorithm based on the basic genetic algorithm. In this algorithm, many variables such as sort order of parts, mirror parameters, rotation angle and storage parameters of part center position were coded by decimal system, the initial population was randomly generated, and the selection, crossover and mutation operations were carried out. Finally, the layout effect diagram of expanded part was obtained. By comparing the layout effect of basic genetic algorithm and multivariate coding genetic algorithm, it can be seen that the multivariate coding genetic algorithm can accurately characterize the geometric shapes and positional features of layout parts, ensure the high efficiency of layout, and improve the material utilization rate of layout. At the same time, the feasibility and effectiveness of the multivariate coding genetic algorithm is verified, which has important guiding significance for optimizing the manufacturing process and improving the production efficiency of sheet metal parts.

基金项目：

湖南省教育厅科学研究项目（19C0623）

作者简介：张京京（1987-），女，硕士，讲师，E-mail：940112291@qq.com；通信作者：陈晓鹏（1988-），男，硕士，工程师，E-mail：136843440@qq.com

参考文献：

[1]刘玲玲,赵罘,龚堰珏.基于BL-NFP神经网络算法的二维不规则钣金零件排样[J].锻压技术,2021,46(12):54-60.

Liu L L, Zhao F,Gong Y J. Layout for two-dimensional irregular sheet metal parts based on BL-NFP neural network algorithm[J].Forging & Stamping Technology,2021,46(12):54-60.

[2]凌晗,刘楠嶓,武照云,等.基于改进遗传算法的矩形件排样优化研究[J].现代制造技术与装备,2017，(9):66-67，69.

Ling H，Liu N F，Wu Z Y，et al. Research on rectangular packing optimization based on improved genetic algorithm[J]. Modern Manufacturing Technology and Equipment，2017，(9):66-67，69.

[3]马康. 基于分布估计算法的矩形件排样优化问题研究[D].镇江：江苏科技大学,2016.

Ma K.Research of Rectangular Packing Methods Based on Estimation of Distribution Algorithms[D]. Zhenjiang：Jiangsu University of Science and Technology,2016.

[4]薛文奎.遗传算法在农业移动机器人路径规划中的应用——基于矩阵二进制编码[J].农机化研究,2019,41(12):69-73.

Xue W K. Application of genetic algorithm in path planning of agricultural mobile robot-Based on matrix binary code[J]. Journal of Agricultural Mechanization Research，2019,41(12):69-73.

[5]孟子暄.基于遗传算法的钣金件下料问题优化研究[J].现代工业经济和信息化,2018,8(15):24-25.

Meng Z X. Research on optimization of sheet metal cutting problem based on genetic algorithm [J].Modern Industrial Economy and Informationization,2018,8(15):24-25.

[6]吴阳.并行遗传退火算法的圆形件下料问题求解[D]. 南京：广西大学,2018.

Wu Y.Parallel Genetic Annealing Algorithm for Solving Circle Piece Blanking Problems[D].Nanning：Guangxi University,2018.

[7]曾晓亮,吴琼,袁旭华.二维矩形件排样问题的自适应多岛遗传算法优化[J].锻压技术,2020,45(12):53-58.

Zeng X L，Wu Q，Yuan X H. Adaptive multi-island genetic algorithm optimization on layout problem for 2D rectangular parts[J]. Forging & Stamping Technology,2020,45(12):53-58.

[8]计明军,邓文浩,郭文文,等.改进填充算法在矩形件排样中的应用[J].数学的实践与认识,2019,49(4):139-150.

Ji M J，Deng W H，Guo W W，et al. Improved filling algorithm for rectangle packing problem[J]. Mathematics in Practice and Theory,2019,49(4):139-150.

[9]穆丹.基于遗传算法的木板材优化下料问题研究[D].哈尔滨：东北林业大学,2017.

Mu D.Research on the Wood Board Cutting-stock Problem Based on Genetic Algorithm[D]. Harbin：Northeast Forestry University，2017.

[10]陈洁琼,樊留群,杨剑.遗传算法在风管制造排样中的应用[J].制造业自动化,2017,39(5):113-115,143.

Chen J Q，Fan L Q，Yang J. Genetic algorithm application in air duct sheet metal parts layout[J]. Manufacturing Automation,2017,39(5):113-115,143.

[11]董功云,陈进,王鸿超.矩形件排样的流程和算法设计[J].轻工机械,2016,34(6):57-61.

Dong G Y，Chen J，Wang H C. Process and algorithm design of layout of rectangular plate[J]. Light Industry Machinery,2016,34(6):57-61.

[12]王伟,杜向阳.基于捕食搜索策略的遗传算法在矩形件优化排样中的研究[J].中国农机化学报,2019,40(2):157-162.

Wang W，Du X Y. Genetic algorithm based on predatory search strategy for rectangular optimal layout[J]. Journal of Chinese Agricultural Mechanization,2019,40(2):157-162.

[13]Vitayasak S, Pongcharoen P. Consistency on optimal ga parameter setting on various characteristics of layout design problem[A]. Proceedings of 2015 International Conference on Power Electronics and Energy Engineering（PEEE 2015）[C]. Hong Kong：2015.

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