Intelligent Classification of Tungsten Inert Gas Welding Defects: A Transfer Learning Approach

Abstract

Automated and intelligent classification of defects can improve productivity, quality, and safety of various welded components used in industries. This study presents a transfer learning approach for accurate classification of tungsten inert gas (TIG) welding defects while joining stainless steel parts. In this approach, eight pre-trained deep learning models (VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, and DenseNet169) were explored to classify welding images into two-class (good weld/bad weld) and multi-class (good weld/burn through/contamination/lack of fusion/lack of shielding gas/high travel speed) classifications. Moreover, four optimizers (SGD, Adam, Adagrad, and Rmsprop) were applied separately to each of the deep learning models to maximize prediction accuracies. All models were evaluated based on testing accuracy, precision, recall, F1 scores, training/validation losses, and accuracies over successive training epochs. Primary results show that the VGG19-SGD and DenseNet169-SGD architectures attained the best testing accuracies for two-class (99.69%) and multi-class (97.28%) defects classifications, respectively. For “burn through,” “contamination,” and “high travel speed” defects, most deep learning models ensured productivity over quality assurance of TIG welded joints. On the other hand, the weld quality was promoted over productivity during classification of “lack of fusion” and “lack of shielding gas” defects. Thus, transfer learning methodology can help boost productivity and quality of welded joints by accurate classification of good and bad welds.