Journal of Nondestructive Evaluation最新文献_第5页

Magnetic Flux Leakage Testing for Internal and External Defect Identification in Rotating Pipe Inspections 漏磁检测在旋转管检测中内外缺陷识别中的应用

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-08-18 DOI: 10.1007/s10921-025-01245-2

Jung-Min Jo, Seung-Ahn Chae, Gwan-Soo Park, Dae-Yong Um

{"title":"Magnetic Flux Leakage Testing for Internal and External Defect Identification in Rotating Pipe Inspections","authors":"Jung-Min Jo, Seung-Ahn Chae, Gwan-Soo Park, Dae-Yong Um","doi":"10.1007/s10921-025-01245-2","DOIUrl":"10.1007/s10921-025-01245-2","url":null,"abstract":"<div><p>This study proposes a magnetic flux leakage inspection capable of identifying internal and external defects in rotating pipe inspections. The proposed identification between internal and external defects employs the effect of motion-induced eddy current that has been an adverse effect on the conventional magnetic flux leakage testing. A three-dimensional finite element analysis was conducted to assess the feasibility of detecting and classifying these defects. Two hall sensors, symmetrically positioned from the pole structure, exhibit asymmetric defect signals with inverse signal variations for the internal and external defects. Simulation studies were performed to investigate the effect of flux density and rotational speed on defect signals. A prototype sensor was fabricated, and the measurement shows peak-to-peak variations as − 43.1% for internal defects and + 25.7% for external defects, indicating a strong correlation with the simulation results. These findings suggest that the proposed inspection can represent an effective alternative to the conventional ultrasonic testing for monitoring pipe integrity at the pipe production stage.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861607","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

Laser Ultrasonic Wavefield Reconstruction and Defect Detection Using Physics-Informed Neural Networks 基于物理信息神经网络的激光超声波场重建与缺陷检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-23 DOI: 10.1007/s10921-025-01241-6

Yingfan Song, Bin Xu, Yun Zou, Gaofeng Sha, Liang Yang, Guixi Cai, Yang Li

{"title":"Laser Ultrasonic Wavefield Reconstruction and Defect Detection Using Physics-Informed Neural Networks","authors":"Yingfan Song, Bin Xu, Yun Zou, Gaofeng Sha, Liang Yang, Guixi Cai, Yang Li","doi":"10.1007/s10921-025-01241-6","DOIUrl":"10.1007/s10921-025-01241-6","url":null,"abstract":"<div><p>Laser ultrasonic (LU) testing has attracted considerable attention in the fields of material characterization and defect detection due to its non-destructive nature. However, acquiring a complete wavefield using LU typically requires significant time and resources, motivating the development of more efficient sampling strategies. In this study, a novel approach based on Physics-Informed Neural Networks (PINNs) is proposed to reconstruct the full Lamb wavefield from sparsely sampled experimental data. By embedding the governing physical laws of wave propagation into the neural network framework, the PINN model is trained to infer the wavefield characteristics from a limited number of measurements. Notably, the proposed method successfully reconstructs the complete Lamb wavefield with an accuracy of 88% while using only one-sixteenth of the full dataset. The results highlight the potential of PINNs to improve both the efficiency and accuracy of wavefield reconstruction, offering a promising solution to the limitations of conventional LU testing.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145169087","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

Application of the Improved YOLOv8 Algorithm for Small Object Detection in X-ray Weld Inspection Images 改进YOLOv8算法在x射线焊缝检测图像小目标检测中的应用

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-10 DOI: 10.1007/s10921-025-01235-4

Wenwen Lu, Haoyuan Zheng, Shouzhen Xiao, Weihua Xue, Shaobin Yang

{"title":"Application of the Improved YOLOv8 Algorithm for Small Object Detection in X-ray Weld Inspection Images","authors":"Wenwen Lu, Haoyuan Zheng, Shouzhen Xiao, Weihua Xue, Shaobin Yang","doi":"10.1007/s10921-025-01235-4","DOIUrl":"10.1007/s10921-025-01235-4","url":null,"abstract":"<div><p>The adoption of machine vision to replace manual inspection in X-ray non-destructive testing (NDT) for image defect detection has emerged as a significant trend in the advancement of welding defect detection. In this paper, an enhanced strategy is proposed to address the issue of low detection accuracy of YOLOv8 in X-ray weld defect detection. An extra tiny object detection head is added to the detection head, which enables more accurate capture of extremely small defect features, effectively expanding the lower detection limit and significantly enhancing the detection capability for extremely small weld defects. By employing serpentine deformable convolution, the model dynamically adjusts its receptive field, enabling it to flexibly adapt to variations in crack morphology, thereby improving the detection capability for small objects with special shapes. The integration of an advanced BiFPN structure enables three-level feature fusion, optimizing the detection performance for medium and large objects across multiple scales, and expanding the upper detection range. The results show that the proposed improvement strategy achieves the maximum detection scale while also significantly improving detection accuracy, with the overall mAP@50% reaching 97.2%, an increase of 17.1%. The proposed strategy in this study significantly improves the accuracy of weld defect detection. It also enhances the detection performance for small targets with specific shapes, extremely small defects, and expands the model’s scale adaptability. Validation experiments conducted on the GDXray weld dataset further demonstrate its effectiveness.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164472","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

Dual-Mode Nondestructive Uniformity Characterization of Special-Shaped Ceramic Matrix Composites 异型陶瓷基复合材料的双模无损均匀性表征

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-10 DOI: 10.1007/s10921-025-01230-9

Yintang Wen, Yuhang Du, Wenhan Qu, Jia Gao, Yuyan Zhang

{"title":"Dual-Mode Nondestructive Uniformity Characterization of Special-Shaped Ceramic Matrix Composites","authors":"Yintang Wen, Yuhang Du, Wenhan Qu, Jia Gao, Yuyan Zhang","doi":"10.1007/s10921-025-01230-9","DOIUrl":"10.1007/s10921-025-01230-9","url":null,"abstract":"<div><p>Ceramic matrix composites represent a novel type of high-temperature structural material. Defects such as porosity, delamination, and cracking generated during the fabrication process significantly impact the structural uniformity and performance, especially in the case of irregular shapes where this issue becomes more pronounced. Conventional methods that rely on overall grayscale values often fail to quantify structural density differences in irregular components. To address this, we propose a dual-mode uniformity characterization method based on block grayscale difference calculation. Considering the high porosity of ceramic matrix composites and the characteristics of pore defects, the tomography image after pore removal is obtained based on the adaptive threshold algorithm. These images are then partitioned into blocks based on their spatial positions, and the average grayscale values of each block are calculated to achieve a digital representation of the composite material’s uniformity. Furthermore, three-dimensional reconstruction of the average grayscale using volume rendering algorithms provides a visual representation of the structural distribution for intuitive analysis. Test analysis of a U-shaped C<sub>f</sub>/SiC specimen yielded maximum grayscale values for blocks of 134.81, minimum grayscale values of 92.24, and grayscale differences between blocks of 42.57, effectively characterizing the digital differences in structural uniformity among various blocks of the specimen. The visualization results of three-dimensional reconstruction and color mapping depict the spatial distribution characteristics of the structural specimen. This method offers a new approach to characterizing the uniformity of irregular-shaped ceramic matrix composites.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164277","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

Surface Extraction for Industrial CT Based on Surface Tracking 基于表面跟踪的工业CT表面提取

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-05 DOI: 10.1007/s10921-025-01223-8

Lin Xue, Zhaoxiang Li

引用次数: 0

Intelligent Flaw Detection in Eddy Current Inspection Data Through Machine Learning Model 基于机器学习模型的涡流检测数据智能探伤

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-03 DOI: 10.1007/s10921-025-01229-2

Tikesh Kumar Sahu, S. Thirunavukkarasu, Anish Kumar

引用次数: 0

Multi-Physics Modeling of Above-Ground Electromagnetic Inspection on Underground Pipeline 地下管线地上电磁检测多物理场建模

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-03 DOI: 10.1007/s10921-025-01227-4

Ahmed Khaled, Rami El-Haibe, Karolos Grigoriadis, Yingjie Tang, Matthew Franchek, Keng Yap, Debartha Bag

{"title":"Multi-Physics Modeling of Above-Ground Electromagnetic Inspection on Underground Pipeline","authors":"Ahmed Khaled, Rami El-Haibe, Karolos Grigoriadis, Yingjie Tang, Matthew Franchek, Keng Yap, Debartha Bag","doi":"10.1007/s10921-025-01227-4","DOIUrl":"10.1007/s10921-025-01227-4","url":null,"abstract":"<div><p>This study explores the effectiveness of electromagnetic-based non-destructive evaluation (NDE) for above-ground inspection of underground pipelines to detect corrosion defects. The above-ground electromagnetic inspection method involves measuring the magnetic field induced by an alternating current (AC) passed through the buried pipeline and analyzing the resulting electromagnetic field perturbations under various frequencies to identify defects. Finite Element Analysis (FEA) simulations using ANSYS Electronics Desktop were conducted to model the electromagnetic field around pipelines induced by a given AC signal with various frequencies through the pipeline with and without defects. The numerical simulations indicate the capability of detecting magnetic field perturbations caused by wall defects from above-ground sensors, even at a distance of one meter above the pipeline. However, sensing ability at the nano-Tesla level is required. The thresholds for such perturbations to indicate a pipeline defect were also numerically studied. The study also evaluated the impact of sensor movement and its sensitivity effect on the electromagnetic field and then the Low-High Frequency Method was introduced to mitigate potential false positives due to sensor displacement. The results highlight the potential of electromagnetic NDE for reliable and efficient pipeline monitoring, contributing to enhanced safety and operational efficiency in the oil and gas sector. Future experimental validation will be performed to validate the numerical solutions, quantify the effectiveness and optimize defect detection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01227-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161418","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

CNN-CHD: Combining Clustering Hierarchical Divisive and CNN for Enhanced Weld Defect Detection CNN- chd：结合聚类、层次分裂和CNN的焊缝缺陷检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-03 DOI: 10.1007/s10921-025-01225-6

Dalila Say, Mounira Tarhouni, Salah Zidi, Soliman Aljarboa

{"title":"CNN-CHD: Combining Clustering Hierarchical Divisive and CNN for Enhanced Weld Defect Detection","authors":"Dalila Say, Mounira Tarhouni, Salah Zidi, Soliman Aljarboa","doi":"10.1007/s10921-025-01225-6","DOIUrl":"10.1007/s10921-025-01225-6","url":null,"abstract":"<div><p>Weld defects, such as cracks, porosity, and inclusions, can significantly compromise the structural integrity of welds, making their early and accurate detection crucial in various industrial sectors. In this research, we propose a comprehensive methodology that combines the Clustering Hierarchical Divisive (CHD) method with convolutional neural networks (CNNs) to enhance defect detection accuracy. Our approach begins with the creation of a robust database, leveraging Generative Adversarial Networks (GANs) for data augmentation, which allowed us to generate a more diverse and representative dataset essential for effective model training. The CHD method performs an initial segmentation of weld images, clustering them into coherent groups based on confusion matrix analysis, ensuring that each cluster corresponds to distinct defect classes. Subsequently, the clustered images are processed using CNNs, renowned for their powerful classification capabilities. This hybrid approach effectively captures the variability of weld defects, significantly improving detection accuracy while reducing similarities among defects. Our proposed CNN-CHD method offers a more efficient pipeline for defect identification in welding applications, and its potential to enhance quality control in industrial practices.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161208","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

From Uncertainty to Calibration: Online Pose Estimation of an Industrial Twin Robotic Computed Tomography System with Unknown Spheres 从不确定性到校准：一个未知球体的工业孪生机器人计算机断层扫描系统的在线姿态估计

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-01 DOI: 10.1007/s10921-025-01222-9

Niklas Handke, Yiqun Q. Ma, Anton Weiss, Simon Wittl, Rebecca Wagner, Gabriel Herl

{"title":"From Uncertainty to Calibration: Online Pose Estimation of an Industrial Twin Robotic Computed Tomography System with Unknown Spheres","authors":"Niklas Handke, Yiqun Q. Ma, Anton Weiss, Simon Wittl, Rebecca Wagner, Gabriel Herl","doi":"10.1007/s10921-025-01222-9","DOIUrl":"10.1007/s10921-025-01222-9","url":null,"abstract":"<div><p>Robotic CT systems offer several advantages over conventional systems due to their high flexibility. They can perform almost any CT trajectory and are particularly well suited for region-of-interest (ROI) scans of objects that exceed the size limitations of conventional CT systems. However, robot-based manipulators have a significantly lower absolute positioning accuracy compared to conventional manipulators, necessitating additional calibration methods to refine the geometric information about the spatial position and orientation of the X-ray source and detector for each projection for higher resolution reconstructions. We propose a geometric calibration method for CT systems with twelve degrees of freedom that does not require additional calibration scans. The method is easy to use, computationally efficient, and supports continuous CT trajectories. It utilises spheres with unknown positions that are attached to the specimen. The calibration process is divided into two stages. First, the spatial positions of the spheres are estimated using the initial geometric information and the acquired projections. Second, these estimates serve as input to an iterative optimisation that calibrates each projection individually. The applicability of the proposed method is demonstrated through simulations and real-world scans using a twin robotic CT system. Both quantitative and qualitative evaluations show a significant improvement in scan quality, comparable to results obtained via offline calibration. Moreover, evaluations on simulated data confirm the method’s robustness even for systems with positioning errors in the millimetre range. This novel online calibration technique is computationally efficient, compatible with highly flexible CT systems, and holds promise for enabling future mobile CT applications.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01222-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160855","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

Stacked Inexact Augmented Lagrangian Multiplier with Sparse and Low-Rank Matrix Decomposition: A Novel Low Sampling Vibration Signal Denoising Strategy for Enhancing Feature Quality to Predict Health State of Bearings Using Machine Learning Model 基于稀疏低秩矩阵分解的叠置非精确增广拉格朗日乘子：一种利用机器学习模型提高特征质量预测轴承健康状态的低采样振动信号去噪策略

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-07-01 DOI: 10.1007/s10921-025-01212-x

Gaurav Popli, Avishek Mukherjee, Surjya Kanta Pal

{"title":"Stacked Inexact Augmented Lagrangian Multiplier with Sparse and Low-Rank Matrix Decomposition: A Novel Low Sampling Vibration Signal Denoising Strategy for Enhancing Feature Quality to Predict Health State of Bearings Using Machine Learning Model","authors":"Gaurav Popli, Avishek Mukherjee, Surjya Kanta Pal","doi":"10.1007/s10921-025-01212-x","DOIUrl":"10.1007/s10921-025-01212-x","url":null,"abstract":"<div><p>Induction motors are essential in industrial systems but are susceptible to bearing failures, leading to unanticipated downtime and elevated maintenance expenses. Early identification of these defects is challenging, as vibration signals are sometimes obscured by industrial noise. Prior decomposition-based denoising techniques encounter difficulties with low-sampling-rate data due to their dependence on high time–frequency resolution and their sensitivity to noise and parameter adjustments. These approaches frequently struggle to discern subtle fault signs in imprecise or noisy data. This paper introduces a stacked augmented Lagrangian multiplier (ALM)-assisted sparse and low-rank matrix decomposition (SLD) method that resolves these constraints. The approach isolates sparse fault-related features from background noise without necessitating high-resolution inputs or substantial parameter tuning, hence preserving diagnostic accuracy at low sampling rates. By conducting local segment analysis, it improves the visibility of defect frequencies at different motor speeds. The integration of retrieved features with artificial neural networks (ANNs) results in enhanced classification accuracy. This research provides practical benefit by facilitating scalable, real-time condition monitoring through low-cost data collecting devices, therefore substantially decreasing operational expenses and enhancing reliability across extensive industrial fleets.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160537","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