Component Placement Process Optimization for Dual-Gantry Placement Machine Based on Hybrid Genetic Algorithm

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Component Placement Process Optimization for Dual-Gantry Placement Machine Based on Hybrid Genetic Algorithm

更新时间：2025-07-09

- Component Placement Process Optimization for Dual-Gantry Placement Machine Based on Hybrid Genetic Algorithm
- Vol. 43, Issue 5, Pages: 80-84(2009)
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH166;TP18
- Online First：10 May 2009，
  
  Published：2009
- 稿件说明：
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Component Placement Process Optimization for Dual-Gantry Placement Machine Based on Hybrid Genetic Algorithm[J]. 2009, 43(5): 80-84.
DOI：

Component Placement Process Optimization for Dual-Gantry Placement Machine Based on Hybrid Genetic Algorithm[J]. 2009, 43(5): 80-84. DOI：

摘要

针对复合式贴片机的贴装过程优化问题

将其分解为元件分配、供料器布置和元件取贴顺序3个子问题.在分析实际工程应用的基础上

以动臂的负荷平衡和最小移动距离为目标

建立了双动臂转塔式贴片机的贴装过程集成优化模型

采用遗传算法和元件分配启发式方法相结合的方式实现了贴装过程的优化.算法中提出了一种分段二元实数编码方法

在一条染色体中同时描述了元件分配、供料器布置和元件取贴顺序.针对提出的编码方式

采用改进的顺序交叉和自适应的变异操作

结合轮盘赌选择和精英选择策略以及适应度函数的设计

在满足贴片机运动机构约束条件的情况下

使得遗传搜索能够快速地进入有效解空间进行搜索.实例计算结果表明

该算法能实现多动臂转塔式贴片机贴装过程的优化

从而提高印刷电路板的装配效率.

Abstract

Based on the analysis of dual-gantry turret machine

the optimization problem of placement process is decomposed to component allocation

feeder arrangement and component placement sequence. Via an engineering analysis

an integrated optimization model of dual-gantry multi-head turret placement machine is formulated. The minimum displacement of arm and workload balance are taken as the objective. Combined with heuristic method and genetic algorithm(GA)

hybrid GA(HGA)is adopted to optimize the placement process. A two-dimensional piece-wise coding method is proposed. In the individual chromosome

component allocation

feeder arrangement and component placement sequence is described simultaneously. HGA contains improved order crossover

adaptive mutation and contains a reasonable fitness function. Under the condition of constraint satisfying of movement mechanisms

HGA guarantees that the genetic research can enter the available solution space quickly and research the optimal solution. The component allocation

component placement sequence and feeder arrangement are optimized simultaneously to improve the printed circuit board assembly efficiency.

关键词

Keywords

references

WILLIAM H, JI Ping. A genetic algorithm to optimize the component placement process in PCB assembly [J]. International Journal of Advanced Manufacturing Technology, 2005, 26(12): 1397-1401.

DUMAN E, OR I. The quadratic assignment problem in the context of the printed circuit board assembly process [J]. Computer and Operations Research, 2007, 34(1):163-179.

ONG N S, KHOO L P. Sequence placement planning for high-speed PCB assembly machine [J]. Integrated Manufacturing Systems, 2002, 13(1):35-46.

WILHELM W E, TARMY P K. Circuit card assembly on tandem turret-type placement machines [J]. IIE Transactions, 2003, 35(7):627-645.

DEO S, JAVADPOUR R, KNAPP G M. Multiple setup PCB assembly planning using genetic algorithms [J]. Computers and Industrial Engineering, 2002, 42(1):1-16.

ELLISS K P, VITES F J, KOBZA J E. Optimizing the performance of a surface mount placement machine [J]. IEEE Transactions on Electronics Packaging Manufacturing, 2001, 24(3): 160-170.

WILLIAM H, JI Ping. Component scheduling for chip shooter machines: a hybrid genetic algorithm approach[J]. Computers and Operations Research,2003,30(14): 2175-2189.

杜轩,李宗斌,高新勤,等.基于遗传算法的转塔式贴片机贴装过程优化 [J]. 西安交通大学学报, 2008, 42(3):295-299.

DU Xuan, LI Zongbin, GAO Xinqin. Component placement process optimization for chip shooter machine based on genetic algorithm [J]. Journal of Xi'an Jiaotong University, 2008, 42(3):295-299.

SIEMENS. Specification siplace S-27 HM [EB/OL].(2007-08-01)[2007-10-01].http:∥ea.automation.siemens.com.

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