Ndt & E International最新文献

{"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}

{"title":"Eliminating the effect of couplant on ultrasonic characterisation of solids: Experimental and theoretical investigations","authors":"V. Tinard,&nbsp;C. Fond,&nbsp;P. François","doi":"10.1016/j.ndteint.2025.103385","DOIUrl":"10.1016/j.ndteint.2025.103385","url":null,"abstract":"<div><div>Up to now, in-situ monitoring of earthquake-resistant bearings for bridges or nuclear power plants has been limited to visual inspection or inspection of witness samples. In the context of exploring non-destructive measurement methodologies for such monitoring, ultrasonic techniques have emerged as a promising approach. However, ensuring optimal coupling between the transducer or waveguide and the material to be characterised is of crucial importance to guarantee the reliability of the measurement. Typically, the coupling agent employed acts as a liquid, which can exert a considerable effect upon measurement outcomes. The objective of this study is to examine the impact of the coupling agent on the assessment of the mechanical properties of viscoelastic solids. Polymer in rubbery state (polychloroprene) was characterised using the Surface Reflection Method with two different coupling agents (honey and a commercial coupling agent named SWC2). The results obtained in terms of storage and loss moduli exhibited considerable variability depending on the couplant under consideration. G′ values were found to be 258 MPa with honey and 287 MPa with SWC2 at 2 MHz. In addition, G″ values were determined to be 213 MPa with honey and 501 MPa with SWC2 at the same frequency. Consequently, an inverse method based on combining the results obtained using two different couplants was developed. This method was used to determine the storage and loss moduli of polychloroprene at high frequencies in a reliable way, which, to the authors' knowledge, does not exist in the literature: for the storage modulus, the estimated value is higher than those obtained previously (G’ = 339 ± 17 MPa), and the loss modulus lies between the two values aforementioned (G” = 312 ± 40 MPa).</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103385"},"PeriodicalIF":4.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642192","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":"Singular-value Gain Compensation: Robust and efficient GPR preprocessing method enhancing zero-shot underground object segmentation by Segment-Anything Model","authors":"Jingzi Chen,&nbsp;Tsukasa Mizutani","doi":"10.1016/j.ndteint.2025.103366","DOIUrl":"10.1016/j.ndteint.2025.103366","url":null,"abstract":"<div><div>This paper introduces Singular-value Gain Compensation (SGC), a robust preprocessing method for Ground Penetrating Radar (GPR) that integrates Singular Value Decomposition (SVD) and Time Gain Compensation (TGC). SGC effectively enhances the signal-to-noise ratio while maintaining weak signal integrity, facilitating the application of pretrained zero-shot segmentation models. Through extensive evaluations using simulated and real-world data, SGC demonstrates superior performance in image quality and segmentation accuracy compared to traditional methods, showing the improvements of +3.1 dB in PSNR and 23% in segmentation’s IoU in complex simulated scenarios. It also shows 20% and 14% improvements in pipe and void segmentations on real-world data. Additionally, SGC is computationally efficient, reducing both time and memory requirements, making it practical for large-scale infrastructure assessments. The method’s efficacy in enhancing GPR image analysis without extensive computational resources marks a significant advancement in ground penetrating radar preprocessing and provide more possibilities for future research in the downstream tasks combining with recent deep learning models.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103366"},"PeriodicalIF":4.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686390","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":"Enhanced structural evaluation of reinforced concrete using first generation gamma computed tomography and super-resolution imaging","authors":"Wilson Macharia Kairu ,&nbsp;Siphila Wanjiku Mumenya ,&nbsp;Kenneth Dickson Njoroge ,&nbsp;Prabhu Rajagopal","doi":"10.1016/j.ndteint.2025.103387","DOIUrl":"10.1016/j.ndteint.2025.103387","url":null,"abstract":"<div><div>This paper discusses a novel approach utilising first-generation Gamma Computed Tomography (fgen-GCT) for the non-destructive evaluation of reinforced concrete structures. Through both laboratory experiments and field applications, the effectiveness of fgen-GCT in imaging internal structural elements, such as reinforcement bars and conduits, is demonstrated. Additionally, the integration of a Super-Resolution Generative Adversarial Network (SRGAN) is explored to enhance image resolution from lower-resolution scans, thereby reducing the acquisition time and minimising radiation exposure risks. The results indicate the potential of fgen-GCT to improve structural assessment accuracy, thereby contributing to enhanced infrastructure inspection and maintenance practices.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103387"},"PeriodicalIF":4.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686961","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":"Three-dimensional defect reconstruction in carbon fiber-reinforced composites with temporally non-uniform pulsed thermography data","authors":"L. Gahleitner ,&nbsp;G. Mayr ,&nbsp;P. Burgholzer ,&nbsp;U. Cakmak","doi":"10.1016/j.ndteint.2025.103363","DOIUrl":"10.1016/j.ndteint.2025.103363","url":null,"abstract":"<div><div>This research study presents an efficient and comprehensive three-dimensional photothermal reconstruction scheme for non-uniform pulsed thermography data utilizing the virtual wave concept. For the transformation of temperature signals into virtual wave signals, different physical-based temporally non-uniform sampling techniques for the temperature and the virtual wave signal are used. Furthermore, time reversal is implemented for three-dimensional defect reconstruction in the context of temporally non-uniform sampling. The proposed reconstruction scheme is experimentally demonstrated using carbon fiber-reinforced composite samples with foreign object defects as well as flat bottom holes. To sum up, a reconstruction technique is demonstrated that allows increasing the computational efficiency, while retaining the quality of the reconstruction results and simultaneously reducing data storage requirements compared to conventional temporally uniform sampling.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103363"},"PeriodicalIF":4.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619537","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":"Super-resolution enhancement of X-ray microscopic images of solder joints","authors":"Dorottya Varga ,&nbsp;Zsolt Szabó ,&nbsp;Péter János Szabó","doi":"10.1016/j.ndteint.2025.103382","DOIUrl":"10.1016/j.ndteint.2025.103382","url":null,"abstract":"<div><div>This study explores the application of single-image super-resolution (SISR) to enhance 3D X-ray microscopy (XRM) images for solder joint inspection. Three different voxel sizes (2 μm, 1.5 μm, and 0.5 μm) were used to scan solder joints, with the highest resolution (0.5 μm) serving as the training dataset by pairing it with bicubic down-sampled images. Two enhanced sub-pixel convolutional neural network (ESPCN) models were developed and trained to reconstruct high-resolution (HR) images. The models – ESPCN1 and ESPCN2 – were evaluated using structural similarity index (SSIM) and learned perceptual image patch similarity (LPIPS). Both models achieved high peak signal-to-noise ratio (PSNR) values of 40.01 dB (ESPCN1) and 40.33 dB (ESPCN2), demonstrating strong image reconstruction capabilities. Super-resolution models led to a significant increase in SSIM (12.0 %) and LPIPS (13.8 %) values compared to lower-resolution scans, with ESPCN1 excelling at the 2 μm voxel size and ESPCN2 showing better performance for 1.5 μm. Both models exhibited comparable results when compared to ground truth 0.5 μm scans, with ESPCN2 marginally outperforming ESPCN1 in comparison to cross-sectional evaluations. Overall, the study demonstrates that super-resolution models can enhance the quality of lower-resolution XRM images, offering comparable performance to high-resolution scans while reducing scanning time, thus proving the utility of SISR in industrial inspection applications.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103382"},"PeriodicalIF":4.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619539","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":"Structure-guided piezoelectric transducer for unidirectional excitation and reception of Rayleigh waves","authors":"Heming Guo ,&nbsp;Gang Wang ,&nbsp;Hongchen Miao","doi":"10.1016/j.ndteint.2025.103384","DOIUrl":"10.1016/j.ndteint.2025.103384","url":null,"abstract":"<div><div>Rayleigh waves are crucial for nondestructive testing (NDT) of thick-walled structures. Unidirectional propagation of Rayleigh waves simplifies signal interpretation by minimizing unwanted reflections. Unidirectional Rayleigh waves are commonly generated using wedge transducers based on Snell's law. However, due to the large wave impedance difference between the wedge and the waveguide, there is a transmission loss at the interface. This work proposed a structure-guided transducer (SGT) for unidirectional excitation and reception of Rayleigh waves. The SGT consists of a wedge-shaped substrate and thickness-shear piezoelectric wafer. The substrate is fabricated using ferromagnetic material, which is impedance-matched to the steel structure for Rayleigh waves. The substrate facilitates the automatic control of the guided waves, guaranteeing the unidirectional capabilities of the SGT. Subsequent to the theoretical design, the performance of the SGT was assessed via finite element simulations and experiments. Results confirmed the generation of pure Rayleigh waves with a unidirectionality of approximately 13 dB. Moreover, the SGT can also act as a sensor to selectively capture Rayleigh waves from a specific direction, efficiently filtering out waves from other directions. The design of the SGT is both straightforward and conducive to manufacturing. It can prevent energy loss resulting from impedance mismatches between the wedge and the test structure. Additionally, the magnetic substrate of the SGT facilitates effortless attachment to steel structures, thereby simplifying installation and relocation. With these advantages, the SGT is highly likely to have substantial potential for applications in NDT.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"154 ","pages":"Article 103384"},"PeriodicalIF":4.1,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629070","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}