Weld defect identification and characterization in radiographic images using deep learning: Review

Abstract

Defects in the welds degrade the quality of the weld. Weld defect identification is a challenging task in the industry because of the wide range of weld imperfections. Weld defect detection using radiographic images is an effective technique for achieving good weld quality in shipbuilding and aerospace applications. Foreign inclusions, cracks and pores are examples of welding joint imperfections. Several appropriate computer-based image processing techniques have made the detection of weld defects possible. It is challenging because weld imperfection can show various sizes, shapes, contrasts and locations in radiography images. The accuracy of this inspection process is more dependent on various external factors and is also time-consuming. Automatic weld defect detection is needed by analyzing the images obtained directly from digital radiographic systems. This paper uses a unique image-based approach to a small batch of X-ray imaging datasets to investigate a potential solution for weld defect identification. This article compares a deep learning network's performance for various parameter and hyper-parameter combinations. Also it compares the traditional approaches of defect detection using manual inspection method, feature-based defect identification, and finally deep - learning based approach on several types of weld defects in various industrial applications. This comparative analysis concludes that deep learning-based approaches have achieved more accuracy as compared to conventional techniques. This research paper also highlights a few challenges and future directions in welding area.

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

“…In recent years, there have been notable breakthroughs in the field of deep learning (DL), which have resulted in the development of creative solutions for several industrial problems, including tire defect detection [6]. One of the most common branches of deep learning is the convolutional neural network (CNN), which is currently regarded as a highly popular deep learning architecture frequently utilized in many classification applications [7,8].…”