基于改进粒子群算法的电路板测试路径规划

针对飞针测试机检测电路板时检测时间长、测试效率低、单针检测容易撞针等问题,提出了一种基于改进粒子群算法的测试路径规划算法.首先,使用分区检测的方式解决两针相撞问题;其次,提出一种改进的粒子群算法,在粒子群算法的基础上加入混沌初始化公式用于约束和更新搜索的最大速度,引入遗传算法的交叉、变异的思想,改进粒子群算法易于趋于局部最优的缺陷,提升了算法的全局搜索能力.与粒子群算法、遗传算法进行有效性的对比分析与实机测试.结果表明:此算法可以有效解决测试时两针相撞问题;比起其他两种算法改进粒子群算法在更少的迭代数的同时全局搜索能力更强,可以减少30%算法运算时间、降低10%的测试距离,具有一定的工程应用价值.

Test Path Planning of Circuit Board Based on Improved Particle Swarm Optimization Algorithm

Flying probe testing machines have a long detection time and low test efficiency, and their probes are easy to strike in single probe detection when detecting circuit boards. Therefore, a test path planning algorithm based on an improved particle swarm optimization algorithm is proposed. Firstly, the collision between two probes is solved by partition detection. Secondly, an improved particle swarm optimization algorithm is proposed, and a chaotic initialization formula is added to constrain and update the maximum speed of search based on the particle swarm optimization algorithm. In addition, the idea of crossover and variation of the genetic algorithm is introduced to improve some defects that the particle swarm optimization algorithm tends to fall into local optimum, which enhances the global search ability of the algorithm. The effectiveness of the proposed algorithm, particle swarm optimization algorithm, and genetic algorithm is compared and analyzed, and real machine tests are carried out. The results show that the proposed algorithm can effectively solve the collision between two probes during the tests. Compared with the other two algorithms, the improved particle swarm optimization algorithm has a stronger global search ability while reducing the number of iterations, and it can reduce the algorithm operation time by 30% and the test distance by 10%, which has a certain engineering application value.