Radiographic Non-destructive Testing Research Articles

Recent Development of Halide Perovskite Materials and Devices for Ionizing Radiation Detection.

Ionizing radiation such as X-rays and γ-rays has been extensively studied and used in various fields such as medical imaging, radiographic nondestructive testing, nuclear defense, homeland security, and scientific research. Therefore, the detection of such high-energy radiation with high-sensitivity and low-cost-based materials and devices is highly important and desirable. Halide perovskites have emerged as promising candidates for radiation detection due to the large light absorption coefficient, large resistivity, low leakage current, high mobility, and simplicity in synthesis and processing as compared with commercial silicon (Si) and amorphous selenium (a-Se). In this review, we provide an extensive overview of current progress in terms of materials development and corresponding device architectures for radiation detection. We discuss the properties of a plethora of reported compounds involving organic-inorganic hybrid, all-inorganic, all-organic perovskite and antiperovskite structures, as well as the continuous breakthroughs in device architectures, performance, and environmental stability. We focus on the critical advancements of the field in the past few years and we provide valuable insight for the development of next-generation materials and devices for radiation detection and imaging applications.

The effects of undercut geometry on the static stress concentration factor of welds

Undercutting is a welding defect that appears as a groove in the base metal directly along the edges of the weld metal. It is inevitable in fillet and butt joints if improper welding parameters are used in the operation. It is a discontinuity in the welding that produces stress concentration and lowers the strength of the weld. The stress concentration factor of an undercut is due to the reinforcement angle, undercut width, undercut depth and undercut root radius. In this study, 20 mm thick mild steel plates were welded by automatic gas metal arc welding with the carbon dioxide (CO2) shielding gas process. A single-V butt joint was obtained after welding. Before welding, 30° groove angles were obtained by milling on the longitudinal side of each workpiece. Two plates were welded with several passes. After welding, the weldment was tested with a radiographic non-destructive testing process. A defect-free weldment was obtained. Then, standard tensile test samples were machined from the weldment. A groove was drilled in the heat-affected zone, adjacent to the weld metal boundary on every tensile test sample. Each groove resembled an undercut. The length, root radius and depth of grooves were varied. Then, the samples were broken on a tensile test machine. From the test results, the static stress concentration factor of each groove was calculated. The effects of groove geometry on stress concentration factors were determined.

Radiographic Nondestructive Testing Simulations Using gate Software Toolkit

Abstract A workspace is created within the geant4 Application for Tomographic Emission (gate) software toolkit for radiographic nondestructive testing (NDT) simulations. The workspace contains models for X-ray and gamma ray emissions, test sample geometries, radiation interaction with the test sample, and image receptors to afford a radiographic NDT setup. The setup is then evaluated by simulating radiographic testing (RT) of stainless-steel step-wedges and a carbon steel flawed welded plate. The results are validated by comparing a simulated step-wedge image to an experimental radiograph. The workspace can be advantageous to the industrial radiography specialists who are unfamiliar with medical imaging software. Running the simulation does not require working knowledge of gate; the open-source software toolkit.

Multi‐step radiographic image enhancement conforming to weld defect segmentation

To improve the accuracy of automatic defect segmentation in radiographic non-destructive testing and evaluation, the authors proposed a multi-step radiographic image enhancement algorithm (MSRE) conforming to weld defect segmentation. In this algorithm, the requirement of defect segmentation is fully considered when enhancing a radiographic testing image. The first-step enhancement is performed by linear weighting between an original radiographic image and its contrast-limited adaptive histogram equalisation image. Anisotropic diffusion filtering is used for simultaneously smoothing the weighted image and preserving the defect edges very well. Then, the filtered image is enhanced by a fuzzy enhancement algorithm as the final-step enhancement and hence, obtaining a new image with high contrast, high definition, and strong edge intensity. The authors compared MSRE with adaptive histogram equalisation, fuzzy enhancement, global contrast enhancement, and local contrast enhancement algorithms and evaluated its performance by using indicators such as image contrast, definition, edge intensity, and information entropy. Furthermore, the authors compared the segmentation results of the enhanced images to further study the algorithm's effect on weld defect segmentation. Experimental results reveal that the quality of enhanced images is significantly improved by MSRE, and the image enhanced by MSRE has an high relative segmentation accuracy (RSA) of more than 95%.

A Simple Algorithm for Defect Detection From a Few Radiographies

The paper concerns the radiographic nondestructive testing of well-manufactured objects. The detection of anomalies is addressed from the statistical point of view as a binary hypothesis testing problem with nonlinear nuisance parameters. A new simple and numerically stable detection scheme is proposed as an alternative to the conventional generalized likelihood ratio test which becomes untractable in the non-linear case. This original decision rule detects the anomalies with a loss of a negligible part of optimality with respect to an optimal test designed for the “closest” hypothesis testing problem with linear nuisance parameters. The inspection of nuclear fuel rods is discussed to illustrate the relevance of the proposed theoretical solution.

Segmentation of radiographic images using fuzzy c-means algorithm

Radiographic non-destructive testing is often used for detecting welding defects. Due to the degraded quality and the small size of the defects, X-ray films are sometimes difficult to interpret. The interpretation of such images is often affected by a human operator's subjectivity. Digital image processing techniques allow -the interpretation to be automated. A key step in the automated interpretation process is the segmentation of indications from the background. In this paper, a segmentation method based on filzzy c-means algorithm is applied to the radiographic image. In the proposed method, firstly top-hat, bottom-hat filter and adaptive wavelet thresholding are used to improve the quality of the radiographic image. Then, a fuzzy c-means algorithm is applied to segment the radiographic image. The experimental results show that the proposed method gives good performance for radiographic images.