Automatic compact camera module solder joint inspection method based on machine vision. (1st October 2022)

• Record Type: Journal Article
• Authors: Peng, Yanhua; Yan, Yipu; Chen, Guoyu; Feng, Biao
• Is Part Of: Measurement science & technology. Volume 33:Number 10(2022)
• Publication Date: 2022-10-01
• Subjects: CCM solder joints -- imaging optimization -- feature recognition -- defect classification; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac769a
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Because of the contradiction between the production requirements of compact camera modules (CCMs) to achieve high efficiency and quality and the low efficiency and poor accuracy of traditional solder joint inspection methods, an automatic inspection method of CCM solder joint based on machine vision is proposed. After optimizing the imaging parameters according to the CCM inspection process, the region of interest is dynamically identified based on feature matching and image enhancement methods to remove background interference. On this basis, an improved adaptive particle swarm optimization is used to optimize the kernel extreme learning machine to automatically classify the solder joint defects. Experimental results showed that with its low latency, high precision and robustness, the CCM surface solder joint defect detection and classification method based on machine vision can effectively solve the problem of low efficiency and high cost of the current CCM solder joint defect detection technology.