Journal of Nondestructive Evaluation最新文献

{"title":"Analysis of Reliability and Effectiveness of Repeated Inspections Based on Correlated Probability of Detection","authors":"Seonhwa Jung,&nbsp;Youngchan Kim,&nbsp;Dooyoul Lee,&nbsp;Joo-Ho Choi","doi":"10.1007/s10921-024-01112-6","DOIUrl":"10.1007/s10921-024-01112-6","url":null,"abstract":"<div><p>Repeated inspections have been reported to improve the reliability of nondestructive inspection and can be evaluated by multiplying the likelihood function. However, repeated inspections conducted by a single inspector may not be independent, because the subsequent inspections may be influenced by previous inspection results. The probability of detection (POD) quantifies the sensitivity and reliability of an inspection system. In this study, eddy-current inspection data were used to assess the effect of repeated inspections on POD improvement. Specifically, repeated measures correlation (RMC) analysis was performed, which did not violate the assumption of independence to analyze intra-individual association, considering the nonindependence of repeated measures. Nonindependent repeated inspections performed using a combination of two datasets reduced the uncertainty in POD. Moreover, RMC yielded further improvements in POD and reduced the uncertainty.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227467","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}

{"title":"Learning Scatter Artifact Correction in Cone-Beam X-Ray CT Using Incomplete Projections with Beam Hole Array","authors":"Haruki Hattori,&nbsp;Tatsuya Yatagawa,&nbsp;Yutaka Ohtake,&nbsp;Hiromasa Suzuki","doi":"10.1007/s10921-024-01113-5","DOIUrl":"10.1007/s10921-024-01113-5","url":null,"abstract":"<div><p>X-ray cone-beam computed tomography (CBCT) is a powerful tool for nondestructive testing and evaluation, yet the CT image quality can be compromised by artifact due to X-ray scattering within dense materials such as metals. This problem leads to the need for hardware- and software-based scatter artifact correction to enhance the image quality. Recently, deep learning techniques have merged as a promising approach to obtain scatter-free images efficiently. However, these deep learning techniques rely heavily on training data, often gathered through simulation. Simulated CT images, unfortunately, do not accurately reproduce the real properties of objects, and physically accurate X-ray simulation still requires significant computation time, hindering the collection of a large number of CT images. To address these problems, we propose a deep learning framework for scatter artifact correction using projections obtained solely by real CT scanning. To this end, we utilize a beam-hole array (BHA) to block the X-rays deviating from the primary beam path, thereby capturing scatter-free X-ray intensity at certain detector pixels. As the BHA shadows a large portion of detector pixels, we incorporate several regularization losses to enhance the training process. Furthermore, we introduce radiographic data augmentation to mitigate the need for long scanning time, which is a concern as CT devices equipped with BHA require two series of CT scans. Experimental validation showed that the proposed framework outperforms a baseline method that learns simulated projections where the entire image is visible and does not contain scattering artifacts.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01113-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219621","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}

{"title":"Classification of Practical Floor Moisture Damage Using GPR - Limits and Opportunities","authors":"Tim Klewe,&nbsp;Christoph Strangfeld,&nbsp;Tobias Ritzer,&nbsp;Sabine Kruschwitz","doi":"10.1007/s10921-024-01111-7","DOIUrl":"10.1007/s10921-024-01111-7","url":null,"abstract":"<div><p>Machine learning in non-destructive testing (NDT) offers significant potential for efficient daily data analysis and uncovering previously unknown relationships in persistent problems. However, its successful application heavily depends on the availability of a diverse and well-labeled training dataset, which is often lacking, raising questions about the transferability of trained algorithms to new datasets. To examine this issue closely, the authors applied classifiers trained with laboratory Ground Penetrating Radar (GPR) data to categorize on-site moisture damage in layered building floors. The investigations were conducted at five different locations in Germany. For reference, cores were taken at each measurement point and labeled as (i) dry, (ii) with insulation damage, or (iii) with screed damage. Compared to the accuracies of 84 % to 90 % within the laboratory training data (504 B-Scans), the classifiers achieved a lower overall accuracy of 53 % for on-site data (72 B-Scans). This discrepancy is mainly attributable to a significantly higher dynamic of all signal features extracted from on-site measurements compared to laboratory training data. Nevertheless, this study highlights the promising sensitivity of GPR for identifying individual damage cases. In particular the results showing insulation damage, which cannot be detected by any other non-destructive method, revealed characteristic patterns. The accurate interpretation of such results still depends on trained personnel, whereby fully automated approaches would require a larger and diverse on-site data set. Until then, the findings of this work contribute to a more reliable analysis of moisture damage in building floors using GPR and offer practical insights into applying machine learning to non-destructive testing for civil engineering (NDT-CE).</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01111-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920509","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}

{"title":"Automated Detection of Delamination Defects in Composite Laminates from Ultrasonic Images Based on Object Detection Networks","authors":"Xiaoying Cheng,&nbsp;Haodong Qi,&nbsp;Zhenyu Wu,&nbsp;Lei Zhao,&nbsp;Martin Cech,&nbsp;Xudong Hu","doi":"10.1007/s10921-024-01116-2","DOIUrl":"10.1007/s10921-024-01116-2","url":null,"abstract":"<div><p>Ultrasonic testing (UT) is a commonly used method to detect internal damage in composite materials, and the test data are commonly analyzed by manual determination, relying on a priori knowledge to assess the status of the specimen. In this work, A method for the automatic detection of delamination defects based on improved EfficientDet was proposed. The Swin Transformer block was adopted in the Backbone part of the network to capture the global information of the feature map and improve the feature extraction capability of the whole model. Meanwhile, a custom block was added to prompt the model to extract object features from different receptive fields, which enriches the feature information. In the Neck part of the network, the adaptive weighting was used to keep the features that were more conductive to the prediction object, and desert or give smaller weights to those features that were not desirable for the prediction object. Two kinds of specimens were prepared with embedded artificial delamination defects and delamination damage caused by low-velocity impacts. Ultrasonic phased array technology was employed to investigate the specimens and the amount of data was increased by the sliding window approach. The object detection model proposed in this work was evaluated on the obtained dataset and delamination in the composites was effectively detected. The proposed model achieved 98.97% of mean average precision, which is more accurate compared to ultrasonic testing methods.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926399","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}

{"title":"Analysis of a Prototype Multi-Detector Fast-Neutron Radiography Panel","authors":"Christian X. Young,&nbsp;Chloe A. Browning,&nbsp;Ryan J. Thurber,&nbsp;Matthew R. Smalley,&nbsp;Michael J. Liesenfelt,&nbsp;Jason P. Hayward,&nbsp;Nicole McFarlane,&nbsp;Michael P. Cooper,&nbsp;Jeff R. Preston","doi":"10.1007/s10921-024-01106-4","DOIUrl":"10.1007/s10921-024-01106-4","url":null,"abstract":"<div><p>A multi-detector fast neutron radiography panel was built using the previous work on scalable neutron radiography using the IDEAS ROSSPAD readout module. A new aluminum housing was built to accommodate a large number of detectors tiled together. Additional changes to startup and processing code were made to operate the detector as one cohesive unit. Spatial resolution of the full panel using Cs-137 gammas was reported to be 0.42 line pairs per centimeter at 90% MTF and 2.09 line pairs per centimeter at 10% MTF. Three neutron radiographs generated using a Cf-252 fission neutron source were used to determine the spatial resolution of the panel for neutrons. The experiments had 90% MTF values of 0.24, 0.3, and 0.27 line pairs per centimeter and 10% MTF values of 1.30, 1.46, and 1.40 line pairs per centimeter. An example neutron radiograph was also used to prove that the radiography panel can perform true neutron radiography.\u0000</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01106-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939333","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}

{"title":"Bayesian-Network-Based Evaluation for Corrosion State of Reinforcements Embedded in Concrete by Multiple Electrochemical Indicators","authors":"Zengwei Guo,&nbsp;Jianhong Fan,&nbsp;Shengyang Feng,&nbsp;Chaoyuan Wu,&nbsp;Guowen Yao","doi":"10.1007/s10921-024-01100-w","DOIUrl":"10.1007/s10921-024-01100-w","url":null,"abstract":"<div><p>The electrochemical indicators including corrosion potential (<i>E</i>_corr), concrete resistivity (<i>ρ</i>), corrosion current density (<i>i</i>_corr), and polarization resistance (<i>R</i>_<i>ρ</i>) are pivotal in the evaluation of the degradation state of reinforcements embedded in concrete. Notwithstanding, extensive investigations traditionally hinge on a singular electrochemical metric for the appraisal of rebar corrosion. The current study transcends this conventional approach by integrating multiple electrochemical detections, significantly improving the accuracy in ascertaining the corrosion status of reinforcing bars within concrete. In this paper, a Bayesian network model is developed, synthesizing results from four electrochemical indictors obtained from published literatures. This model effectively addresses the challenge of integrating unmeasured electrochemical parameters in cases where only a limited set is tested in practical engineering, culminating in a more comprehensive assessment dataset. Further, this study progresses to quantitatively assess the reinforcement corrosion status by devising and fine-tuning an integrated model. The Bayesian network notably excels in extrapolating untested results and accurately determining the thresholds for rebar corrosion status, thus significantly improving the overall assessment capability. The Bayesian network, as employed in this study, computes median <i>E</i>_corr and <i>i</i>_corr values at -282mV and 0.168µA/cm², respectively. These computed values exhibit a deviation within 15% of experimental data, aligning with the uncertainty range stipulated by the ASTM C876-91 standards.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886152","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}

{"title":"The Detection of Local Impact Fatigue Damage on Metal Materials by Combining Nonlinear Acoustic Modulation and Coda Wave Interferometry","authors":"Yuqi Ma,&nbsp;Jianbo Wu,&nbsp;Yanjie He,&nbsp;Zhaoyuan Xu,&nbsp;Suixian Yang","doi":"10.1007/s10921-024-01108-2","DOIUrl":"10.1007/s10921-024-01108-2","url":null,"abstract":"<div><p>Some metal structures in the aerospace and nuclear industries are subjected to repeated impact loads that accumulate microcracks until fracture, called impact fatigue damage, which will compromise the metal structure’s overall strength and fatigue life. The microcracks generated by impact fatigue damage on metal materials are so small that, at present, only some microscopic characterization methods have been used to evaluate its damage level, such as scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), energy X-ray dispersive spectroscopy (EDS), and X-ray Photoelectron Spectroscopy (XPS). There is a lack of more convenient and effective non-destructive testing methods. In this paper, the combination of nonlinear acoustic modulation and coda wave interferometry is used to detect impact fatigue damage on 40Cr steel specimens. The simulation discusses the observability of local elastic modulus reduction caused by impact fatigue damage in nonlinear coda wave interferometry (NCWI). Finally, NCWI experiments were carried out on six 40Cr steel specimens with different impact times. Results show that the proposed method can effectively detect and quantify the metal impact fatigue damage.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797103","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}

{"title":"Feasibility Study on the Use of the Coplanar Capacitive Sensing Technique for Underwater Non-Destructive Evaluation","authors":"M. Mwelango,&nbsp;X. Yin,&nbsp;M. Zhao,&nbsp;Z. Zhang,&nbsp;Z. Han,&nbsp;R. Fan,&nbsp;P. Ma,&nbsp;X. Yuan,&nbsp;W. Li","doi":"10.1007/s10921-024-01104-6","DOIUrl":"10.1007/s10921-024-01104-6","url":null,"abstract":"<div><p>Recent advancements in non-destructive evaluation (NDE) techniques have demonstrated potential in assessing underwater structural integrity. However, evolving maritime structures demand more efficient, user-friendly, and technologically advanced underwater NDE methods. Building on successful applications in air as a medium, this paper explores the feasibility of utilizing coplanar capacitive sensors to gauge structural integrity in underwater environments, drawing on assertions made by pioneering scholars. The study employs simulations, complemented by experimental validation, to assess its viability. With artificial surface defects in both non-conducting and conducting specimens, this study conducts a comprehensive comparison of the performance between the bare-electrode and insulated-electrode coplanar capacitive sensor (CCS). The outcomes affirm the viability of utilizing the technique for underwater NDE. Notably, the study reveals that electrical conductivity is a significantly influential factor, and there are discernible differences in response between the two sensor configurations. The nature of the response in non-conducting materials is intricately tied to the dominant sensitivity value region. However, detecting defects in conducting materials poses a challenge in some instances. Overall, results show that defect detection, characterisation and imaging under water are feasible, thereby emphasizing the techniques potential for underwater NDE. This study broadens underwater NDE knowledge and offers a viable alternative for inspecting structures and equipment in underwater environments.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797195","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}

{"title":"A Method for Semi-automatic Mode Recognition in Acoustic Emission Signals","authors":"Ruben Büch,&nbsp;Benjamin Dirix,&nbsp;Martine Wevers,&nbsp;Joris Everaerts","doi":"10.1007/s10921-024-01085-6","DOIUrl":"10.1007/s10921-024-01085-6","url":null,"abstract":"<div><p>Acoustic emission (AE) is a non-destructive technique that relies on monitoring naturally occurring sources of high frequency ultrasound in components and structures. Ultrasonic waves propagate in the form of different wave modes—for instance Lamb waves in thin plates, or Rayleigh and P- and S- waves in bulk structures. Those wave modes have different properties, but also contain information regarding the source of the naturally occurring wave. Manually, the wave modes can be recognized by comparing a time–frequency representation of the signal to the dispersion curves expected in the tested object. For analyzing a large number of signals, this manual mode recognition becomes a tedious process. This paper proposes a method to automate the wave mode recognition based on some minimal knowledge of the occurring wave modes. As inputs, only the propagation speed of the possible wave modes and the source position need to be provided along with a limited set of reference wavelets for each wave mode. Cross-correlation of a signal with a reference wavelet of a mode reduces the signal to a limited number of peaks that may delineate the start of the mode. Using other signals from the same event but from different sensors, velocities are calculated for each peak in order to select the peak that corresponds to the arrival of the mode under investigation. To validate the method, a dataset was recorded based on four types of out-of-plane sources: Hsu-Nielsen sources of 0.3 and 0.5 mm, sensor pulse signals and AEs from melting ice. Since the presented dataset was recorded on a plate, the aim of the validation was to recognize the zero-order symmetrical and anti-symmetrical Lamb modes. The results of the proposed mode recognition method applied to this dataset are compared with results from manual mode recognition. For Hsu-Nielsen sources, the succes rate is found to be above 95%. For narrow-band pulsed signals or for AEs from melting ice with a low signal-to-noise ratio, succes rates between 75 and 80% relative to manual mode recognition are reported.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742516","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}

{"title":"Adaptive and High-Precision Isosurface Meshes from CT Data","authors":"Lin Xue,&nbsp;Jialong Xu,&nbsp;Kai Ma,&nbsp;Zhaoxiang Li,&nbsp;Jingtao Wang","doi":"10.1007/s10921-024-01102-8","DOIUrl":"10.1007/s10921-024-01102-8","url":null,"abstract":"<div><p>This paper proposes a method for obtaining adaptive and high-precision surface meshes directly from Industrial computed tomography (ICT) projection data. Firstly, an adaptive volume octree is recursively constructed from top to bottom using a two-stage geometric error metric function. The CT values and gradient values at the nodes are computed using the Feldkamp–Davis–Kress (FDK) reconstruction algorithm and its derivatives, achieving sub-voxel precision. Next, feature vertices are calculated based on Quadratic error functions (QEFs), and a dual mesh is constructed. Finally, Hermite interpolation is used to determine the iso-surface vertices, and the Convex Contouring lookup table is employed to accurately extract the iso-surface contours, resulting in high-precision and crack-free surface meshes. Experimental results show that the surface meshes generated by the proposed method exhibit superior dimensional accuracy, form and position accuracy, and surface model accuracy compared to traditional methods, and the dimensional accuracy has been enhanced by approximately 10–30%.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641102","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}