Detection of solder paste defects with an optimization‐based deep learning model using image processing techniques

Abstract

Purpose In the production processes of electronic devices, production activities are interrupted due to the problems caused by soldering defects during the assembly of surface-mounted elements on printed circuit boards (PCBs), and this leads to an increase in production costs. In solder paste applications, defects that may occur in electronic cards are usually noticed at the last stage of the production process. This situation reduces the efficiency of production and causes delays in the delivery schedule of critical systems. This study aims to overcome these problems, optimization based deep learning model has been proposed by using 2D signal processing methods. Design/methodology/approach An optimization-based deep learning model is proposed by using image-processing techniques to detect solder paste defects on PCBs with high performance at an early stage. Convolutional neural network, one of the deep learning methods, is trained using the data set obtained for this study, and pad regions on PCB are classified. Findings A total of six types of classes used in the study consist of uncorrectable soldering, missing soldering, excess soldering, short circuit, undefined object and correct soldering, which are frequently used in the literature. The validity of the model has been tested on the data set consisting of 648 test data. Originality/value The effect of image processing and optimization methods on model performance is examined. With the help of the proposed model, defective solder paste areas on PCBs are detected, and these regions are visualized by taking them into a frame.