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首页> 外文期刊>Automation Science and Engineering, IEEE Transactions on >Solving Feeder Assignment and Component Sequencing Problems for Printed Circuit Board Assembly Using Particle Swarm Optimization
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Solving Feeder Assignment and Component Sequencing Problems for Printed Circuit Board Assembly Using Particle Swarm Optimization

机译：使用粒子群算法解决印刷电路板装配中的馈线分配和组件排序问题

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摘要

Printed circuit board assembly (PCBA) is a process of connecting various electronic components through printed circuit boards (PCBs). Due to the need to assemble a lot of components and PCBs at the same time, the PCBA process tends to become the bottleneck in an assembly line. Many assembly firms have thus introduced automated PCBA machines to expedite this process. However, to best operate these machines, effective PCBA planning is still required. Some nature-inspired metaheuristics such as simulated annealing and genetic algorithm (GA) have been increasingly used for the PCBA planning. Also, we find that particle swarm optimization (PSO) has never been employed to deal with the feeder assignment problem (FAP) and component sequencing problem (CSP) at the same time, though it has been regarded as a good competitor to GAs. In this paper, we developed two PSO-based approaches to deal with the two problems simultaneously for a chip shooter machine. In addition, we have conducted experiments to compare the two PSO-based approaches with two GA-based approaches. The experimental results showed that PSO2, the PSO-based approach with sigmoid functions, outperformed others in terms of assembly cycle time. The comparison with an exact approach further shows that PSO2 has a high rate to find the optimalear-optimal solution.
机译：印刷电路板组件（PCBA）是通过印刷电路板（PCB）连接各种电子组件的过程。由于需要同时组装许多组件和PCB，PCBA流程往往成为组装线的瓶颈。因此，许多组装公司都引入了自动化PCBA机器来加快这一过程。但是，为了使这些机器最佳运行，仍然需要有效的PCBA规划。一些自然启发的元启发法，例如模拟退火和遗传算法（GA），已越来越多地用于PCBA规划。同样，我们发现，尽管粒子群优化（PSO）被认为是GA的良好竞争者，但它从未同时用于处理馈线分配问题（FAP）和组件排序问题（CSP）。在本文中，我们开发了两种基于PSO的方法来同时解决切屑射出机的两个问题。此外，我们进行了实验，以比较两种基于PSO的方法和两种基于GA的方法。实验结果表明，基于PSO的具有S型功能的PSO2在装配周期时间方面优于其他PSO2。与精确方法的比较进一步表明，PSO2具有较高的速率来找到最佳/接近最佳解。

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