Journal of Nondestructive Evaluation最新文献

Debond Detection and Quantification in Honeycomb Sandwich Structure Using Low Frequency Guided Waves 基于低频导波的蜂窝夹层结构脱粘检测与量化

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-25 DOI: 10.1007/s10921-025-01288-5

M. N. M. Patnaik, Renji K, K. V. Nagendra Gopal

{"title":"Debond Detection and Quantification in Honeycomb Sandwich Structure Using Low Frequency Guided Waves","authors":"M. N. M. Patnaik, Renji K, K. V. Nagendra Gopal","doi":"10.1007/s10921-025-01288-5","DOIUrl":"10.1007/s10921-025-01288-5","url":null,"abstract":"<div><p>Detection of debonds in honeycomb sandwich-type structures has been a subject of interest for many researchers. However, detection and quantification of debonds in sandwich structures by methods adaptable for structural health monitoring is still being pursued. Low frequency guided waves are being used for the detection, localization and quantification of the debonds, with some limitations, like quantification of the damage being dependent on the distance of the sensor from the debond, need for a reference signal from the pristine structure etc. The present work investigates the potential of the low frequency guided waves in the detection, localization and quantification of debonds in Honeycomb Sandwich Structures (HSS) and addresses some of these limitations. A unique debond quantification curve for the given HSS is generated using a 3D finite element model and validated experimentally. Unlike in earlier works, these curves are independent of the distance of the sensor from the debond. The methodology is demonstrated in pulse-echo and pitch-catch configurations, and it does not need a reference signal from the pristine structure. The developed method is effective in detecting and quantifying the debond located on both the face skins of the sandwich, with the sensor mounted only on one face skin. An efficient methodology to assess the size of the debond is proposed, based on the results obtained from this study. The guided waves are actuated and sensed by Lead Zirconium Titrate (PZT) transducers which facilitate the implementation of structural health monitoring.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate Detection Method of Si3N4 Wafer Fuzzy Defects Embedded with Hybrid Cross-Attention Mechanism and Feature Gradient Histogram 基于混合交叉注意机制和特征梯度直方图的氮化硅硅片模糊缺陷精确检测方法

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-22 DOI: 10.1007/s10921-025-01289-4

Dahai Liao, Qi Zheng, Changzheng Liu, Kun Hu, Hong Jiang, Chengwen Ma, Wei Wang

{"title":"Accurate Detection Method of Si3N4 Wafer Fuzzy Defects Embedded with Hybrid Cross-Attention Mechanism and Feature Gradient Histogram","authors":"Dahai Liao, Qi Zheng, Changzheng Liu, Kun Hu, Hong Jiang, Chengwen Ma, Wei Wang","doi":"10.1007/s10921-025-01289-4","DOIUrl":"10.1007/s10921-025-01289-4","url":null,"abstract":"<div><p>This study systematically addresses critical challenges associated with blurred edges of wafer defects, including multi-dimensional feature aggregation, abrupt gradient decline, and hierarchical information loss. To tackle these issues, an innovative, precise segmentation method is proposed based on a dual cross-attention mechanism and a feature gradient histogram. Through in-depth analysis of edge-blurring characteristics in wafer defects, a multi-scale embedding matrix equation was formulated to optimize the contour extraction process. Additionally, a multi-level encoder architecture was implemented to enhance the efficiency of edge contour information extraction. To address boundary information loss during segmentation, a boundary gradient optimization model was constructed using multi-scale differential equations, enabling accurate fitting of boundary gradients through feature recombination vectors. Experimental results demonstrate the effectiveness of the proposed wafer defect segmentation approach. The method achieves an average accuracy of 97.51%, with mean intersection-over-union (mIoU) scores exceeding 89% across three distinct types of wafer defect detection tasks. By effectively mitigating the adverse effects of edge blur on segmentation precision, this approach offers a comprehensive solution for wafer defect detection. The contributions of this research not only enhance the accuracy and reliability of defect identification but also provide robust technical support for improving product quality and manufacturing efficiency in high-end semiconductor industries. These advancements hold significant practical value in promoting the high-quality development of the semiconductor sector.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components 结合深度学习和散射控制的高通量x射线CT大型铸造金属构件无损表征

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-21 DOI: 10.1007/s10921-025-01228-3

Amirkoushyar Ziabari, Mohamed Hakim Bedhief, Obaidullah Rahman, Singanallur Venkatakrishnan, Paul Brackman, Peter Katuch

{"title":"Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components","authors":"Amirkoushyar Ziabari, Mohamed Hakim Bedhief, Obaidullah Rahman, Singanallur Venkatakrishnan, Paul Brackman, Peter Katuch","doi":"10.1007/s10921-025-01228-3","DOIUrl":"10.1007/s10921-025-01228-3","url":null,"abstract":"<div><p>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. <i>scatterControl</i>) 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 <i>scatterControl</i> 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<span>(times )</span> 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.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01228-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction 即插即用的2.5D伪影减少先验快速和准确的工业计算机断层扫描重建

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-18 DOI: 10.1007/s10921-025-01239-0

Haley Duba-Sullivan, Aniket Pramanik, Venkatakrishnan Singanallur, Amirkoushyar Ziabari

{"title":"Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction","authors":"Haley Duba-Sullivan, Aniket Pramanik, Venkatakrishnan Singanallur, Amirkoushyar Ziabari","doi":"10.1007/s10921-025-01239-0","DOIUrl":"10.1007/s10921-025-01239-0","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01239-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

X-ray Computed Tomography for Wall Thickness Evaluation and Through-Hole Detection in Additively Manufactured Hollow Lattice Structures 用于增材制造空心晶格结构壁厚评估和通孔检测的x射线计算机断层扫描

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-16 DOI: 10.1007/s10921-025-01269-8

Ibon Holgado, Naiara Ortega, José A. Yagüe-Fabra, Soraya Plaza, Herminso Villarraga-Gómez

{"title":"X-ray Computed Tomography for Wall Thickness Evaluation and Through-Hole Detection in Additively Manufactured Hollow Lattice Structures","authors":"Ibon Holgado, Naiara Ortega, José A. Yagüe-Fabra, Soraya Plaza, Herminso Villarraga-Gómez","doi":"10.1007/s10921-025-01269-8","DOIUrl":"10.1007/s10921-025-01269-8","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01269-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite Element Simulation Study on the Applicability of Thermal Imaging for Detecting Voids Defects in Prestressed Pipes of Bridges Under Hydration Heat Excitation 热成像在水化热激励下检测桥梁预应力管道孔洞缺陷适用性的有限元模拟研究

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-16 DOI: 10.1007/s10921-025-01284-9

Shengli Li, Kai Zhang, Xing Gao, Pengfei Zheng, Can Cui, Yao Lu, Jiahui Ren

{"title":"Finite Element Simulation Study on the Applicability of Thermal Imaging for Detecting Voids Defects in Prestressed Pipes of Bridges Under Hydration Heat Excitation","authors":"Shengli Li, Kai Zhang, Xing Gao, Pengfei Zheng, Can Cui, Yao Lu, Jiahui Ren","doi":"10.1007/s10921-025-01284-9","DOIUrl":"10.1007/s10921-025-01284-9","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Defect Detection Algorithm for Monocrystalline Silicon Solar Cell Modules Based on Image Processing and Deep Learning 基于图像处理和深度学习的单晶硅太阳能电池组件缺陷检测算法

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-16 DOI: 10.1007/s10921-025-01285-8

Deqiang Zhou, Jiahao Zhu, Rongsheng Lu, Xu Liu, Dahang Wan

{"title":"Defect Detection Algorithm for Monocrystalline Silicon Solar Cell Modules Based on Image Processing and Deep Learning","authors":"Deqiang Zhou, Jiahao Zhu, Rongsheng Lu, Xu Liu, Dahang Wan","doi":"10.1007/s10921-025-01285-8","DOIUrl":"10.1007/s10921-025-01285-8","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Super-Resolution X-ray Tomography using MSDnet for Nondestructive Testing of Metallic Lattices: Analysis of Training Dynamics and Strategies 使用MSDnet进行金属晶格无损检测的有效超分辨率x射线断层扫描：训练动力学和策略分析

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-16 DOI: 10.1007/s10921-025-01273-y

Antoine Klos, Luc Salvo, Pierre Lhuissier

{"title":"Effective Super-Resolution X-ray Tomography using MSDnet for Nondestructive Testing of Metallic Lattices: Analysis of Training Dynamics and Strategies","authors":"Antoine Klos, Luc Salvo, Pierre Lhuissier","doi":"10.1007/s10921-025-01273-y","DOIUrl":"10.1007/s10921-025-01273-y","url":null,"abstract":"<div><p>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, <i>in situ</i>, 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.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High Lift-off Detachable Steel Pipe Flaw Detection System with Target-arc probe and Control Center 带有目标圆弧探头和控制中心的高升降可拆卸钢管探伤系统

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-16 DOI: 10.1007/s10921-025-01281-y

Zisheng Guo, Xinhua Wang, Yanhai Zhang, Yuchen Shi, Yuan Zhou, Zeling Zhao, Junfeng Gao, Yuxia Han, Tao Sun

{"title":"High Lift-off Detachable Steel Pipe Flaw Detection System with Target-arc probe and Control Center","authors":"Zisheng Guo, Xinhua Wang, Yanhai Zhang, Yuchen Shi, Yuan Zhou, Zeling Zhao, Junfeng Gao, Yuxia Han, Tao Sun","doi":"10.1007/s10921-025-01281-y","DOIUrl":"10.1007/s10921-025-01281-y","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-destructive Techniques for Thermal Energy Storage Technologies 热能储存技术的非破坏性技术

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-10-10 DOI: 10.1007/s10921-025-01283-w

Joey Aarts, Natalia Mazur, Ruben D’Rose, Stan de Jong, Anders Kaestner, Hartmut Fischer

引用次数: 0