Journal of Nondestructive Evaluation最新文献

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10(times ) while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Cone-beam X-ray computed tomography (XCT) is an essential imaging technique for generating 3D reconstructions of internal structures, with applications ranging from medical to industrial imaging. Producing high-quality reconstructions typically requires many X-ray measurements; this process can be slow and expensive, especially for dense materials. Recent work incorporating artifact reduction priors within a plug-and-play (PnP) reconstruction framework has shown promising results in improving image quality from sparse-view XCT scans while enhancing the generalizability of deep learning-based solutions. However, this method uses a 2D convolutional neural network (CNN) for artifact reduction, which captures only slice-independent information from the 3D reconstruction, limiting performance. In this paper, we propose a PnP reconstruction method that uses a 2.5D artifact reduction CNN as the prior. This approach leverages inter-slice information from adjacent slices, capturing richer spatial context while remaining computationally efficient. We show that this 2.5D prior not only improves the quality of reconstructions but also enables the model to directly suppress commonly occurring XCT artifacts (such as beam hardening), eliminating the need for artifact correction pre-processing. Experiments on both experimental and synthetic cone-beam XCT data demonstrate that the proposed method better preserves fine structural details, such as pore size and shape, leading to more accurate defect detection compared to 2D priors. In particular, we demonstrate strong performance on experimental XCT data using a 2.5D artifact reduction prior trained entirely on simulated scans, highlighting the proposed method’s ability to generalize across domains.

This study investigates the trade-off between minimizing wall thickness and through-hole formation in AlSi10Mg thin hollow lattice structures produced via laser powder bed fusion. X-ray computed tomography (XCT) is employed as a metrological tool to evaluate the effects of laser linear energy density (LED) across conditions ranging from under-melting to over-melting using a single laser track strategy. An XCT-based algorithm is developed for automated through-hole detection, providing quantitative data on through-hole count and size. The algorithm's capability is evaluated through leakage tests. The substitution method, adapted from ISO 15530–3 for tactile coordinate measuring machines (CMM), is employed to assess XCT measurement uncertainty for hollow lattice dimensions. As a new addition to the conventional substitution method, the effects of high-density data generated by XCT are assessed against the calibrated diameters obtained from low-density CMM data and used for the calculation of wall thickness. Experimental results show that under-melting conditions can produce wall thicknesses of 0.135 mm to 0.212 mm, with an exponential increase in through-hole formation as LED decreases. A linear relationship between LED and wall thickness is observed, enabling identification of optimal parameters for producing defect-free thin-walled structures.

In response to the low defect detection accuracy caused by small defect areas and large differences in defect scales in EL images of photovoltaic cell components, a defect detection algorithm for photovoltaic cell modules based on traditional image processing and deep learning is proposed. Firstly, a traditional image processing algorithm is designed to segment the images of the cell modules into individual solar cells for detection. Secondly, the accuracy of defect detection is improved by enhancing the YOLOv8 network. The specific details are as follows: First of all, a dynamic receptive field selection structure called C2DLSK (C2f and Dynamic Large Selective Kernel Module) is designed to replace the C2f module in the backbone. It dynamically selects the appropriate receptive field size for the current target during the feature extraction process to more accurately extract the features of defects. Then the CARAFE (Content-Aware ReAssembly of Features) is used to replace the first nearest-neighbor upsampling module in the neck. At the same time, a bidirectional weighted fusion method called BiConcat is used for feature fusion, which fully utilizes semantic information while enhancing the weight of important features in feature fusion. Finally, the MPDIoU loss function is used to replace the CIoU loss function, further improving the accuracy of defect detection. The experiment shows that under the condition of ensuring real-time detection, the average precision mean average precision (mAP) of this algorithm for defect detection in photovoltaic cell components reaches 85.8%, which is an improvement of 1.9% compared to the original network. Compared with the current mainstream YOLOv3-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv8s, it improves the detection accuracy of photovoltaic cell components by 5.3%, 2.9%, 1.6%, and 0.9% respectively.

Existing infrared thermography detection of cavitation defects in external prestressed pipelines is characterised by a variety of test conditions, making it difficult to explore the applicable conditions thoroughly by experiment. To address this issue, key parameters for the numerical model of hydration heat transfer in grouting material for prestressed pipes were established through the fitting of simulation experiments and field experiments. Subsequently, simulation models were constructed under various conditions to investigate the factors affecting the detection of void defects using infrared thermal imaging, including the presence or absence of steel strands, the size of void defects, the material of the pipeline, and its wall thickness. Our results demonstrate that the presence of steel strands reduces the defect identification capability, with the maximum contrast (MaxΔT) decreasing by 1.117℃ in high polyethylene (HDPE) pipes with a 100% void area. Galvanized steel (GSP) pipes are more difficult to detect than HDPE pipes due to their lower emissivity, particularly in the case of GSP pipes with a 60% void area, where MaxΔT is reduced by 18.96% compared to HDPE pipes. As the size of the void increases, the defect identification capability gradually enhances, and void defects larger than 26% can be detected. For both types of pipes, as the wall thickness increases, the infrared detection time window gradually narrows, with the most significant reduction observed for 30% void defects. This study serves as a reference and provides a theoretical basis for the infrared thermal imaging detection of cavity defects in externally prestressed pipes.

Deep learning-based super-resolution has shown significant potential for enhancing the resolution of low-resolution X-ray computed tomography (CT), a 3D nondestructive imaging technique. It could accelerate CT scanning by several orders of magnitude, opening new possibilities for high-throughput acquisition, in situ, and low-dose experiments. However, current assessments of the resulting image quality often rely on 2D image quality metrics such as PSNR and SSIM, which may not correlate directly with scientific measurements. In the present study, the relationship between training dynamics and image quality in deep learning super-resolution is investigated. A super-resolution method was applied to a stainless steel lattice structure featuring numerous quantifiable defects, imaged with a laboratory CT. The core contribution lies in employing a 2.5D Mixed-Scale Dense neural Network (MSDnet) on experimental data and evaluating its performance using scientific and task-based metrics–specifically related to porosity and surface roughness–while monitoring training dynamics. The results demonstrate that even a standard loss function can effectively reflect network performance dynamic for such material science applications. The best super-resolution accuracy with a magnification factor of 3 was achieved after 100 epochs, generating less than 2 % of missing pores and only around 15 % average error in pore volume. Additionally, practical considerations are proposed to assist in the design of tailored training strategies.

A new steel pipe detection system with a high lift-off detachable enhanced magnetic moment with targeted magnetic core extension and magnetic field focusing probe and its electronic control centre has been proposed to detect in-service steel pipe damage. The system adopts a parabolic arc-arm coil structure to increase the magnetic moment and achieve the target magnetic core extension, supplemented by a targeted compensation coil for targeted magnetic field focusing. We have also developed a supporting circuit control centre to further expand the detection magnitude of data collection. Experimental verification was conducted on 20# steel pipes under various conditions, including different defect scales, defect circumferential positions, pipe wall thicknesses, and operating environments such as thick cladding under extreme conditions. The results showed that the system achieved a probe lift-off height of up to 4.1 times the pipe diameter, detected defects throughout the entire wall thickness, and could discriminate defect severity, increasing the maximum effective detection distance by 28.23% compared to the previous generation system, it can assist in detection of pipelines with ultra-thick cladding or deeper burial dimensions under extreme operating conditions such as high temperature and deep cold. This study discovered the magnetic moment enhancement effect of targeted magnetic core extension and, based on it, optimised the design of the detection end structure. Combined with its corresponding signal characterisation form and circuit control instrument, it contributes to a new way of in-service pipe detection.

The understanding of processes in heat storage materials and reactors can be greatly improved by the use of non-destructive methods that allows the view inside the objects. The advantage of non-destructive methods is that the sample of interest remains intact, experimental changes can be monitored in-situ, and the experiments are less labor intensive. Alongside others, three of the most utilized non-destructive techniques for heat storage systems are discussed: NMR, X-ray imaging, and neutron imaging. The working mechanism and (dis)advantages of these techniques are discussed alongside various applications and examples. This work aims to provide a handle to researchers working in the field of thermal energy storage on how to investigate heat storage materials and reactors in a non-destructive manner.

The need to optimize processes in terms of quality and productivity has led to an increased demand for inspection tools that make the production process faster and more reliable. In the field of welding, one of the biggest challenges is the identification of internal defects. In this context, advanced non-destructive testing techniques have gained prominence in the inspection of joints due to the geometric or metallurgical characteristics of the inspected material. This study evaluated the use of the magnetic eddy current (MEC) technique as an alternative for the inspection of welded joints in thin sheets of high-performance steels by the resistance seam welding (RSEW) process. MEC tests were performed on welded specimens, taken from the production process of high-performance steel coil rolling, using welding parameters both compliant and non-compliant with the production process. In order to evaluate the effectiveness of MEC technique, hot tensile tests were performed to simulate the thermal cycle of the rolling process in the Gleeble thermomechanical system. The results showed a high correlation between the mechanical performance of the joints and the signals obtained through the MEC technique, enabling a future application in industrial environments. In comparison to conventional NDT techniques, MEC proved to be a fast and non-contact metohod with potential for in-line application.

Robotic X-ray imaging systems enable autonomous inspection of the internal integrity of critical infrastructure. However, these systems often suffer from vibrations and unwanted movements that cause motion blur in the resulting radiographs. The impact of this motion blur is often unknown until the first prototype is available and even then requires extensive experimental testing to assess. In addition, tests involving radiation are time-consuming, demand specialized equipment, and pose inherent safety risks. In this work, we propose using X-ray simulation as a tool to complement and replace real images during the development of robotic inspection systems. Our method extends an existing X-ray simulation framework (gVirtualXray) to generate motion-blurred images from any type of motion, which are then validated against experimental data. The approach is applicable to various robotic systems and we demonstrate its use for a decoupled two-drone inspection system for wind turbine blades. This is one of the most demanding applications due to the high degree of freedom of the system components and relatively long exposure times. The simulator provides insights into the motion blur sensitivity of the design, helping among others, to pinpoint the most significant degrees of freedom that affect image quality. Finally, we highlight the potential of the simulator for early estimation of performance limits, generation of training datasets for machine learning algorithms, and optimization of system design without the need for physical prototypes. Both the datasets and the software implementation are publicly available.