Review on Machine Learning Based Welding Quality Improvement
Ik-Su Kim, Moon Gu Lee, Yongho Jeon
Abstract
Artificial intelligence technology is rapidly developing with the improvement of computer performance and the development of various algorithms, and research using artificial intelligence technology is being actively conducted in the field of manufacturing technology. In the field of welding, research on arc welding quality prediction using artificial neural network algorithms (ANN) was mainly conducted in the early stages. Since then, in the case of arc welding quality prediction using a deep neural network (DNN) algorithm, research has been conducted to increase the accuracy by increasing the hidden layer in the ANN algorithm. Recently, many studies have been conducted in the form of predicting and classifying the quality of arc welding based on the convolutional neural network (CNN) algorithm, which is one of the deep learning algorithms. Therefore, in this paper, we review representative algorithms such as ANN, DNN, and CNN applied to the welding field, and introduce studies that have successfully performed bead width prediction, welding quality prediction, and quality classification.