Ndt & E International最新文献

{"title":"Advanced detection and reconstruction of welding defects in irregular geometries using eddy current pulsed thermography","authors":"Dong Wang ,&nbsp;Qiuji Yi ,&nbsp;Yi Liu ,&nbsp;Rongsheng Lu ,&nbsp;Guiyun Tian","doi":"10.1016/j.ndteint.2025.103398","DOIUrl":"10.1016/j.ndteint.2025.103398","url":null,"abstract":"<div><div>The global pursuit of net-zero goals has accelerated the growth of solar energy, positioning photovoltaic (P.V.) systems at the forefront of renewable energy due to their efficient and sustainable electricity conversion. Detecting and classifying welding defects in P.V. systems—especially those with intricate surfaces and varying defect scales—is critical yet complex, as irregular geometries challenge traditional nondestructive testing (NDT) methods. To address this, we present an advanced framework integrating Graph Signal Processing (GSP) into Eddy Current Pulsed Thermography (ECPT). In this method, temporal thermographic sequences are mapped onto a graph topology, where nodes correspond to discrete time points and edges encode temporal dependencies through an adjacency matrix. The graph Laplacian operator, constructed based on the temporal adjacency relationships, is eigen-decomposed to project thermal response dynamics into the graph spectral domain. This transformation enables frequency-resolved analysis of time-evolving thermal waves, inherently isolating defect-induced transient signatures from steady-state thermal backgrounds. In our experimental study, Helmholtz coils generate a uniform current density combined with the novel framework, facilitating effective inspection of intricate surfaces by integrating 3D surface measurements with 3D thermography. Furthermore, we compare this method with other state-of-art algorithms. This multidimensional feature analysis framework robustly separates defect profiles from their backgrounds, addressing the unique challenges posed by the irregular geometries in P.V. systems.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103398"},"PeriodicalIF":4.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In situ nonlinear ultrasonic characterization of slip irreversibility and material hardening in stainless steel 316L","authors":"Changgong Kim,&nbsp;Hyelim Do,&nbsp;Kathryn H. Matlack","doi":"10.1016/j.ndteint.2025.103401","DOIUrl":"10.1016/j.ndteint.2025.103401","url":null,"abstract":"<div><div>This work uses in situ nonlinear ultrasound measurements to study the relationship between the acoustic nonlinearity parameter <em>β</em> and the low cycle fatigue behavior of stainless steel 316L. The measured <em>β</em> shows a rapid decrease during hardening followed by a transition to a slower decrease in <em>β</em> as a function of fatigue cycles. Measurements show this trend is consistent at two different strain amplitudes. By comparing our results with prior work on dislocation characterizations in the same material, we hypothesize that the transition in slopes of <em>β</em> coincides with the planar-to-wavy transition that occurs at the end of hardening. Further, measurement results show that the parameter <em>Δβ</em>_<em>t-c</em>, the difference between <em>β</em> measured after the tension and compression portions of the fatigue cycle, depends on strain amplitude. The dependence of <em>Δβ</em>_<em>t-c</em> on strain amplitude is related to fatigue life through a power law relationship, similar to slip irreversibility. Overall, the results provided in this work suggest that <em>β</em> correlates with characteristics of low cycle fatigue, and thus supports the idea that in situ NLU measurements can eventually be used as a quantitative measure to predict fatigue life.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103401"},"PeriodicalIF":4.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibilty of hyperspectral sensing for detection of early stages of corrosion in sturcutral steel","authors":"Amrita Das,&nbsp;Sattar Dorafshan","doi":"10.1016/j.ndteint.2025.103399","DOIUrl":"10.1016/j.ndteint.2025.103399","url":null,"abstract":"<div><div>The estimated global cost of steel atmospheric corrosion is 3–4 % of the Gross Domestic Product, despite decades of corrosion mitigation practice. Steel, widely used for infrastructure construction, is composed primarily of iron and therefore is extremely vulnerable to atmospheric corrosion. The free iron atoms are highly reactive with water and oxygen molecules abundantly found in the atmosphere. Noncontact sensing for corrosion detectiojn is limited to corrosion with visual manifestation. In this study, diffuse spectroscopy is investigated for the feasibility of corrosion detection before visual manifestation. A36 steel samples were exposed to a corrosive alkaline medium with increasing exposure time. The sample's reflectance spectra were collected in the visual near-infrared (VNIR) and near-infrared (NIR) ranges. The existence of early corrosion products in visual sound samples was verified using X-ray diffraction. Results indicated the presence of corrosion at 600–710 nm in VNIR with a maximum of 97 % change with respect to the uncorroded samples. Hyperspectral images at around 700 nm were analyzed, confirming the presence of corrosion at an incremental rate (2.71–12 %) as exposure increased.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103399"},"PeriodicalIF":4.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An EMPECT-based defect localization method for multilayered structures using high-order statistical analysis and high-frequency component separation strategy","authors":"Shuyan Yang ,&nbsp;Jizhou Zhang ,&nbsp;Siwei Fan ,&nbsp;Guohang Lu ,&nbsp;Shunping Yan ,&nbsp;Zhigeng Fan ,&nbsp;Qiang Wan ,&nbsp;Shuxin Zhang ,&nbsp;Lifeng Li ,&nbsp;Zhenmao Chen ,&nbsp;Shejuan Xie","doi":"10.1016/j.ndteint.2025.103394","DOIUrl":"10.1016/j.ndteint.2025.103394","url":null,"abstract":"<div><div>The multilayered metallic structures are widely used in industrial fields. For these structures, the internal corrosion defects happening in the process of manufacture and service may seriously affect the availability and safety of the structure and even the whole equipment. In this study, to achieve the effective detection and localization of the internal corrosion defects distributed in different layers, based on energy-management pulsed eddy current testing method, a novel high-order signal feature and a new defect localization method on the basis of high-frequency component separation are proposed. The superiority of the novel high-order signal feature is verified through numerical simulation and experiments. Compared with traditional signal feature, the novel high-order signal feature is able to detect smaller-sized defects and has higher signal-to-noise ratio. The validation of the new defect localization method is proved by experiments. The macro accuracy of the proposed localization method is 91.67 % and this method is still effective for the defect with stepped depth.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103394"},"PeriodicalIF":4.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrosion detection from IR thermal images in signed cumulative distribution transform domain","authors":"Jesse S. Morgan ,&nbsp;Abu Hasnat Mohammad Rubaiyat ,&nbsp;K. Peter Judd ,&nbsp;Duy H. Thai ,&nbsp;James Tagert ,&nbsp;Gustavo K. Rohde","doi":"10.1016/j.ndteint.2025.103390","DOIUrl":"10.1016/j.ndteint.2025.103390","url":null,"abstract":"<div><div>This study introduces a novel approach to detect corrosive defects on metal substrates using infrared (IR) thermal images. Among numerous non-destructive techniques, infrared thermography (IRT) is notable for its effectiveness in identifying invisible surface and subsurface corrosion in materials. Existing methods for corrosion detection from IRT images lack the connection to the physical processes governing the emission of heat by defective areas. This paper proposes a transport-based mathematical model to describe the difference in heat-flow characteristics between the non-corroded and corroded material regions. A novel detection technique is then devised, utilizing the signed cumulative distribution transform (SCDT) and a subspace classifier to classify 1D thermal signals derived from IRT image sequences. Experiments demonstrate that the proposed approach is capable of detecting corrosive regions on metal substrates with high accuracy while being data efficient with respect to a number of machine learning-based detection methods.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103390"},"PeriodicalIF":4.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of planar NDE methods for damage in open hole carbon fibre laminates","authors":"Ollie Helps ,&nbsp;Paul Fromme ,&nbsp;Adam Doherty ,&nbsp;Christopher Thornton ,&nbsp;Alan L. Clarke ,&nbsp;Philip J. Withers ,&nbsp;Neha Chandarana","doi":"10.1016/j.ndteint.2025.103393","DOIUrl":"10.1016/j.ndteint.2025.103393","url":null,"abstract":"<div><div>This study evaluates the sensitivity of non-destructive imaging methods to characterise in-plane (delaminations) and out-of-plane (matrix cracking/splitting) damage in open-hole carbon fibre reinforced polymer (CFRP) angle-ply specimens as a function of tensile loading to 86% and 97% of the ultimate tensile stress (UTS). Ultrasound, X-ray radiography (with and without contrast agent), and X-ray phase contrast imaging (XPCi) are compared against X-ray computed tomography (XCT). XPCi shows good sensitivity to out-of-plane (transverse) matrix cracks without the need for a contrast agent but has limited ability to assess in-plane delaminations. Conversely, ultrasonic C-scan demonstrated good accuracy for the quantification and depth estimation of in-plane delaminations, being comparable to segmented XCT data. A combination of ultrasound and XPCi could potentially allow planar NDE of delamination and matrix cracking without the access, throughput, and size limitations inherent in computed tomography or the need for invasive and expensive contrast agents required for radiographic imaging.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103393"},"PeriodicalIF":4.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rebar characterization using dual-polarization GPR","authors":"Hai Liu ,&nbsp;Bin Zhang ,&nbsp;Lewei Yan ,&nbsp;Xu Meng ,&nbsp;Junyong Zhou ,&nbsp;Jie Cui ,&nbsp;Billie F. Spencer","doi":"10.1016/j.ndteint.2025.103391","DOIUrl":"10.1016/j.ndteint.2025.103391","url":null,"abstract":"<div><div>Ground-penetrating radar (GPR) has been extensively employed for inspecting reinforcing bars (rebars) in concrete. Although traditional single-channel GPR can efficiently detect rebar and determine its cover thickness, it is of difficulty to accurately estimate rebar diameter due to its limited resolution. Through calculating the analytical solutions of electromagnetic scattering signals from a metallic cylinder in two orthogonally-polarized channels, this paper proves that their phase difference is sensitive to the cylinder's diameter. Consequently, a method is proposed for estimating rebar diameter from the phase difference measured by a dual-polarization GPR system. The effectiveness of this method is validated through numerical, laboratory, and field experiments. The results indicate that a high accuracy with errors less than 1.3 mm (9.1 %) has been achieved for rebar diameter estimation in various scenarios. Thus, it is concluded that the proposed method offers a practical solution for simultaneous estimation of rebar diameter and cover thickness in reinforced concrete structures using dual-polarization GPR.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103391"},"PeriodicalIF":4.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-machine collaborative automation strategies for ultrasonic phased array data analysis of carbon fibre reinforced plastics","authors":"Vedran Tunukovic ,&nbsp;Shaun McKnight ,&nbsp;Amine Hifi ,&nbsp;Ehsan Mohseni ,&nbsp;S. Gareth Pierce ,&nbsp;Randika K.W. Vithanage ,&nbsp;Gordon Dobie ,&nbsp;Charles N. MacLeod ,&nbsp;Sandy Cochran ,&nbsp;Tom O'Hare","doi":"10.1016/j.ndteint.2025.103392","DOIUrl":"10.1016/j.ndteint.2025.103392","url":null,"abstract":"<div><div>NDE 4.0 represents the integration of recent advancements in robotics, sensor technology, and Artificial Intelligence (AI), transforming and automating traditional NDE in line with Industry 4.0 principles. Despite these advancements, data analysis in NDE is still largely performed manually or with traditional rule-based tools such as signal thresholding. These tools often struggle to effectively manage complex data patterns or high noise levels, leading to unreliable defect detection. Additionally, they require frequent manual adjustments to set appropriate parameters for varying inspection conditions, which can be inefficient and error-prone in dynamic or fast paced environments. In contrast, AI-based analysis tools have demonstrated improvements over traditional methods, offering greater accuracy in defect detection and adaptability to higher variability within captured signals. However, their adoption in industrial settings remains limited due to challenges associated with model trust and their “black box” nature. Additionally, practical guidelines for implementing AI tools into NDE workflow are rarely discussed, motivating this work to explore various integration strategies across different automation levels. Three levels of automation were explored, ranging from basic AI-assisted workflows, where tools provide suggestions, to advanced applications where multiple AI models simultaneously process data in a comprehensive analysis, shifting human operators to a supervisory role. Proposed strategies of AI integration into the NDE automation workflow were evaluated on inspection of two defective complex-geometry carbon fibre-reinforced plastics components, commonly used in aerospace and energy sectors for safety-critical structures such as aircraft fuselages and wind turbine blades. The experimental scans were conducted using a phased array ultrasonic testing roller probe mounted on an industrial manipulator, closely replicating industrial practices, and successfully identifying 36 manufactured defects through a combination of supervised object detection on amplitude C-scans, unsupervised anomaly detection on ultrasonic B-scans, and a self-supervised AI model for processing full volumetric ultrasonic data. This inclusion of multiple AI models led to an improvement of up to 17.2 % in the F1 score compared to single-model approaches. Unlike manual inspections, which take hours for larger components, the proposed approach completes the analysis in 94.03 and 57.01 s for the two inspected samples, respectively.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103392"},"PeriodicalIF":4.1,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DiffUT: Diffusion-based augmentation for limited ultrasonic testing defects in high-speed rail","authors":"Qian Zhang,&nbsp;Kang Tian,&nbsp;Fuben Zhang,&nbsp;Jinlong Li,&nbsp;Kai Yang,&nbsp;Lin Luo,&nbsp;Xiaorong Gao,&nbsp;Jianping Peng","doi":"10.1016/j.ndteint.2025.103388","DOIUrl":"10.1016/j.ndteint.2025.103388","url":null,"abstract":"<div><div>Ultrasonic testing is a widely used nondestructive testing (NDT) method for detecting defects in critical industrial components. However, ultrasonic defect detection in high-speed rail (HSR) systems faces significant challenges due to limited sample availability and complex working conditions. These limitations often lead to subjective judgments by inspectors, increasing the risk of false positives and missed detections. To mitigate data scarcity, this study introduces a diffusion model for data augmentation, applied to real ultrasonic B-scan wheel defect data. By learning the probability and noise distribution through diffusion and reverse diffusion processes, the model generates synthetic data to improve detection accuracy. Experimental results show notable improvements in average precision and recall, increasing from 78.0 % to 66.0 %–93.3 % and 91.5 %, respectively. This method has been successfully deployed in practical applications, with plans for continuous updates as new data becomes available. The study addresses the challenge of limited defect data in industrial NDT and highlights the potential for broader applications in automated defect detection systems.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103388"},"PeriodicalIF":4.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on rubber inclusion defect detection based on terahertz time-domain spectroscopy technology and image fusion","authors":"Jun Hu ,&nbsp;Wennan Liu ,&nbsp;Xiaodong Mao ,&nbsp;Wenping Li ,&nbsp;Jie Chen ,&nbsp;Fengyun Xie","doi":"10.1016/j.ndteint.2025.103389","DOIUrl":"10.1016/j.ndteint.2025.103389","url":null,"abstract":"<div><div>Rubber, in the process of production and service, may contain foreign objects, such as fragments and films. These inclusions can lead to increased wear of rubber products during actual use, resulting in unpredictable safety risks. Therefore, it is essential to perform non-destructive testing to ensure the quality of rubber products. In this paper, a high-precision and non-destructive detection method for rubber inclusion defects based on terahertz technology is proposed. The terahertz time-domain spectroscopy was used to detect prefabricated Silicone Rubber samples with metallic inclusion defects. The obtained terahertz spectral data are optimized by using correction processing algorithms and feature extraction methods. And a highly efficient quantitative detection model for internal inclusion defects in Silicone Rubber is established using machine learning algorithms. Firstly, SNV, AirPLS, AsLS and BEADS are individually applied to preprocess the collected time-domain spectra. Secondly, the features of the spectra are extracted by using CARS, UVE and PCA, respectively. Lastly, Partial Least Squares Regression and Least Squares Support Vector Machines are employed to establish quantitative prediction model of the depth of rubber inclusion defects. The experimental results show that the BEADS and CARS algorithms can greatly reduces the computational load of the model while improving its accuracy. The LS-SVM model has the best prediction effect, and the RMSEP and R_P of the prediction set are 0.0717 and 0.9964, respectively. In addition, the physics-based model of time-of-flight is also employed to calculate the collected time-domain spectra and predict the defect depth, and the RMSEP and R_P of the prediction set are 0.1080 and 0.9986. In terms of terahertz imaging, this paper proposes a high-quality visualization processing scheme for internal inclusion defects in rubber. It utilizes various feature parameters for imaging and employs a combination of grayscale histogram equalization and wavelet transform image fusion methods to achieve high-quality imaging representation of internal inclusion defects in rubber. The THz-TDS techniques enables rapid and non-destructive detection of location, depth and shape of rubber inclusion defect, providing new technological methods for the quality inspection of other polymers.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103389"},"PeriodicalIF":4.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}