Deep learning models for quality estimation of gas tungsten arc welding

Abstract

Gas tungsten arc (GTA) welding is characterized by the use of non-consumable electrodes and shielding gas for effective welding of various metals. Welding quality can be evaluated by a visual test, but it has the disadvantage that accuracy is limited by the experience of the inspector. The quality of GTA welding can be predicted better through deep learning models that use machine vision, image processing, and welding data such as voltage, current, and feeding speed. In this study, artificial neural network (ANN) models and convolutional neural network (CNN) models are developed for estimating welding quality using v-groove GTA welding experiments. The GTA welding system comprises welding equipment and a vision system. After completing the welding experiments, 21 features and 1 label are collected from the welding data. The observations are then normalized, shuffled, and divided into training data and test data. ANN and CNN models are developed with varied architectures, then verified by calculating root mean squared error, mean absolute error, and accuracy. Abstracted ANN models are developed by applying the relative weight and product measure methods to the original ANN models. The abstract ANN models show similar or better accuracy with fewer features.