X-Ray Techniques for Defect Detection in Industrial Components and Materials: A Review

With the growing demand for higher product quality in manufacturing, X-ray non-destructive testing has found widespread application not only in industrial quality control but also in a wide range of industrial applications, owing to its unique capability to penetrate materials and reveal both internal and surface defects. This paper presents a systematic review of recent advances and current applications of X-ray-based defect detection in industrial components. It begins with an overview of the fundamental principles of X-ray imaging and typical inspection workflows, followed by a review of classical image processing methods for defect detection, segmentation, and classification, with particular emphasis on their limitations in feature extraction and robustness. The focus then shifts to recent developments in deep learning techniques—particularly convolutional neural networks, object detection, and segmentation algorithms—and their innovative applications in X-ray defect analysis, which demonstrate substantial advantages in terms of automation and accuracy. In addition, the paper summarizes newly released public datasets and performance evaluation metrics reported in recent years. Finally, it discusses the current challenges and potential solutions in X-ray-based defect detection for industrial components, outlines key directions for future research, and highlights the practical relevance of these advances to real-world industrial applications.