A Study on the Development of Deep Learning Algorithm for Determining External Quality of Welded Parts Using Transfer Learning

Abstract

Recently, deep learning has been applied to various welding techniques, such as laser welding, gas metal arc welding (GMAW), and resistance spot welding, and research on automation and quality prediction is being conducted. Even for GMAW, many researchers have attempted to predict quality through X-ray, current, and voltage measurements. If judgment in real time is not necessary, it is most effective to judge the quality of a welded part using an exterior image. Therefore, in this study, a welded appearance quality judgment model was analyzed using image deep learning. Welding defects were classified into pores, overlaps, craters, melting of the base material, cracks, and undercuts, and were divided into 7 categories including normal ones. In constructing the deep-learning model, transfer learning was performed using existing networks, such as ResNet and AlexNet. To improve the accuracy of deep learning, tests were performed while the optimization technique, maximum number of epochs, and minibatch size were changed. It was confirmed that the accuracy of weld defect prediction improved as the minibatch size increased, and the stochastic gradient descent model had the highest accuracy. Increasing the number of data for learning should make the technique of using images to judge the quality of GMAW welds using the proposed model more widely applicable.