Regarding the issues of missed and false detections in traditional riveting geometric tolerance quality inspection,a riveting quality inspection method based on an improved PSO-SVM is proposed.Utili-zing a strategy of self-adaptive adjustment of inertia weight and selecting appropriate learning factors can effectively improve detection accuracy.For small sample sizes,the least squares SVM algorithm is proposed to enhance computational speed and obtain the optimum solution.The improved PSO algorithm is employed to optimize the penalty factor parameter values and kernel function parameter values of the least squares SVM.Using 6061 aluminum alloy plates,which simulate aircraft thin-walled riveted samples after punching and riveting,images are obtained with a CCD camera equipped with a centrifugal lens and a dataset is es-tablished,thus verifying the effectiveness of the method.
关键词
粒子群优化/最小二乘支持向量机/惯性权重自适应调整/制孔及铆接质量检测
Key words
particle swarm optimization/least squares support vector machine/inertia weight adaptive ad-justment/hole making and riveting quality inspection
维普
万方数据