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Welding technology plays a crucial role in manufacturing, aerospace, construction, and military industries, where the quality of welds directly impacts the safety and reliability of overall structures. Therefore, developing a real-time welding defect detection framework based on deep learning is of paramount importance. However, existing detection methods suffer from limitations such as insufficient real-time performance, high miss rates for small defects, and poor adaptability to complex working conditions. To address these challenges, this paper proposes a welding defect detection framework named WELD-DETR, which incorporates multi-scale feature fusion and multi-kernel perception collaborative optimization. First, we introduce a novel hierarchical feature pyramid (HFPS) structure that effectively combines low-level high-resolution features with high-level semantic features, significantly improving the detection rate of micron-level cracks and pores. Secondly, we innovatively design a multi-kernel perception wavelet convolution (MPWC) module to enhance the model’s ability to respond to edge features and fine textures at various scales. Finally, to further boost the model’s generalization capability, we construct an industrial-grade welding dataset encompassing five common defect types and propose a cross-condition training strategy based on transfer learning. Experimental results show that WELD-DETR achieves an mAP@0.5–0.95 of 98.2%, a precision of 96.8%, and an inference speed of 58 FPS on an RTX 2060 GPU. Moreover, it exhibits superior detection accuracy and real-time performance in complex industrial scenarios such as high noise and strong reflections, outperforming existing state-of-the-art methods in accuracy. These results underscore WELD-DETR’s potential to support intelligent welding quality assurance and process optimization in real-world applications.

In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

In this paper, Artificial Intelligence (AI) algorithms are employed for first, automating the process of creating a large synthetic Total Focusing Method (TFM) imaging dataset using a small set of Finite Element (FE) simulation datasets, and second for the automated defect-recognition (ADR) in butt-welds. In this paper, six types of imaging datasets are created with three approaches. In the first approach, weld TFM images are constructed using ultrasonic A-scan signals obtained from Full Matrix Capture (FMC) performed using FE analysis on models with weld defects (porosity and slag). The second approach generates near real-time weld TFM images by implementing fast deep convolution generative adversarial networks (DCGAN). This second technique permits simulations that are several orders faster when compared to the FE method. In the third approach, noise is extracted from FMC-TFM experimental measurements using the sliding kernel approach, and this noise is supplemented to individual simulated datasets for creating near to realistic scenarios. The first dataset is created using the first approach. The second dataset is created using the second approach, and the third hybrid dataset is a combination of FE and DCGAN weld TFM imaging. The fourth dataset is noise supplemented to FE based dataset. The fifth dataset is generated by adding noise to DCGAN images. The sixth hybrid dataset with noise is a combination of FE and DCGAN weld TFM noise images. AI plays a significant role in object detection and classification through robust feature extraction, reducing human intervention. In this work, for automated weld defect recognition, a convolutional neural network (CNN) is trained using six types of simulation-assisted weld TFM imaging datasets, which improves the reliability and efficiency of welds quality assurance. The mAP value is 85% for the ADR model trained using the hybrid weld TFM dataset with noise. The model prediction on classification on the hybrid dataset for porosity is 0.86 F1-score, and for slag is 0.80 F1-score.

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with a mean intersection over union (IoU) of 0.93 and a mean dice coefficient of 0.96 on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.

The demand for flexible large-area optoelectronic devices has been growing significantly during recent years. Roll-to-roll (R2R) printing facilitates the cost-efficient industrial production of different optoelectronic devices. Nonetheless, the performance of these devices is highly dependent on the printing quality and number of defects of R2R printed conductors. The image processing technique is an efficient nondestructive testing (NDT) methodology used to detect such defects. In this study, a computer vision-based assessment tool was utilized to visualize R2R printed silver conductors’ defects on flexible plastic substrates. A multistage defect detection technique was proposed to detect and classify both printing-induced defects and imperfections as well as the misalignment of the printed conductors with respect to the reference design. The method proved to be a very reliable approach that can be used independently or in conjunction with electrical testing methods for quality assurance purposes during the production of R2R prints.

The demand for flexible large area optoelectronic devices such as organic light-emitting diodes (OLEDs) and organic photovoltaics (OPVs) is growing. Roll-to-roll (R2R) printing enables cost-efficient industrial production of optoelectronic devices. The performance of electronic devices may significantly suffer from local electrical defects. The dark lock-in infrared thermography (DLIT) method is an effective non-destructive testing (NDT) tool to identify such defects as hot spots. In this study, a DLIT inspection system was applied to visualize the defects of R2R printed silver conductors on flexible plastic substrates. A two-stage automated defect recognition (ADR) methodology was proposed to detect and localize two types of typical electrical defects, which are caused by complete or partial breaks on the printed conductive wires, based on localized segmentation and thresholding methods.

In this paper, a state of the art automated defect recognition (ADR) system is presented that was developed specifically for Non-Destructive Testing (NDT) of powder metallurgy (PM) parts using three dimensional X-ray Computed Tomography (CT) imaging, towards enabling online quality assurance and enhanced integrity confidence. PM parts exhibit typical defects such as microscopic cracks, porosity, and voids, internal to components that without an effective detection system, limit the growth of industrial applications. Compared to typical testing methods (e.g., destructive such as metallography that is based on sampling, cutting, and polishing of parts), CT provides full coverage of defect detection. This paper establishes the importance and advantages of an automated NDT system for the PM industry applications with particular emphasis on image processing procedures for defect recognition. Moreover, the article describes how to establish a reference library based on real 3D X-ray CT images of net-shape parts. The paper follows the development of the ADR system from processing 2D image slices of a measured 3D X-ray image to processing the complete 3D X-ray image as a whole. The introduced technique is successfully integrated into an automated in-line quality control system highly sought by major industry sectors in Oil and Gas, Automotive, and Aerospace.

Steel surface defect recognition is an important part of industrial product surface defect detection, which has attracted more and more attention in recent years. In the development of steel surface defect recognition technology, there has been a development process from manual detection to automatic detection based on the traditional machine learning algorithm, and subsequently to automatic detection based on the deep learning algorithm. In this paper, we discuss the key hardware of steel surface defect detection systems and offer suggestions for related options; second, we present a literature review of the algorithms related to steel surface defect recognition, which includes traditional machine learning algorithms based on texture features and shape features as well as supervised, unsupervised, and weakly supervised deep learning algorithms (Incomplete supervision, inexact supervision, imprecise supervision). In addition, some common datasets and algorithm performance evaluation metrics in the field of steel surface defect recognition are summarized. Finally, we discuss the challenges of the current steel surface defect recognition algorithms and the corresponding solutions, and our future work focus is explained.

This article mainly reveals a converter station equipment defect detection system based on the YOLOv5 algorithm. The research encompasses the establishment of a simulated environment for converter station inspection tasks, problem formulation, algorithm design, training process, and experimental evaluation. Key contributions include the enhancement of the YOLOv5 algorithm through improved clustering techniques, dataset preparation, and multi-object detection model training. This highlights the importance of appropriate anchor box initialization in object detection tasks, especially when dealing with small-sized images and varied defect types. In order to verify the correctness of this research, a comparative simulation analysis with mainstream detection algorithms further validates the superiority of the proposed method. The findings of this research contribute valuable insights into the development of AI-driven defect detection systems for power equipment inspection.

Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.