Journal of Nondestructive Evaluation最新文献

Continuation Approach Combined with Semi-Analytical Finite-Element Method for Solving Guided-Wave Dispersion Equation 结合半解析有限元法求解导波色散方程的延拓法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-10 DOI: 10.1007/s10921-025-01194-w

Taizo Maruyama, Kazuyuki Nakahata

引用次数: 0

Unsupervised Stitching Method for Blisk DR Images with Dual-Energy X-Ray Radiography 双能x射线Blisk DR图像的无监督拼接方法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-09 DOI: 10.1007/s10921-025-01177-x

Chao Hai, Mianlong Li, Hong Zhang, Zhaoguang Ma, Min Yang

{"title":"Unsupervised Stitching Method for Blisk DR Images with Dual-Energy X-Ray Radiography","authors":"Chao Hai, Mianlong Li, Hong Zhang, Zhaoguang Ma, Min Yang","doi":"10.1007/s10921-025-01177-x","DOIUrl":"10.1007/s10921-025-01177-x","url":null,"abstract":"<div><p>Traditional image stitching methods rely heavily on the quality of feature matching. However, the blisk Digital Radiography (DR) images tend to exhibit low contrast and repetitive textures, which can frequently cause incorrect alignment during the stitching process. Consequently, this often leads to the appearance of artifacts and distortions in the stitched image. In this paper, we propose an unsupervised image stitching method specifically designed for blisk using dual-energy radiography. Firstly, we adopt a multi-scale enhancement process to enhance image contrast and improve detail clarity in the original images. Secondly, we introduce an unsupervised image stitching method consisting of a coarse alignment module, an image reconstruction module, and a multi-energy image fusion module. The unsupervised coarse registration module automatically learns image features and achieves initial coarse alignment through homography transformation. The unsupervised image reconstruction module learns spatial transformations and deformation patterns of the images, resulting in the reconstruction of pixel-level features in the stitched image and minimizing stitching artifacts. Finally, a dual-energy image fusion module based on Nonsubsampled Contourlet Transform (NSCT) is employed to fuse the stitched images, resulting in globally, high-resolution blisk DR images. Through subjective visual evaluations and quantitative metric analysis, our method demonstrates optimal stitching results on both simulated and real datasets. Our approach effectively preserves image details and structures while minimizing stitching seams, thereby achieving a high-resolution, artifact-free stitching result.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929893","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

SWRD: A Dataset of Radiographic Image of Seam Weld for Defect Detection 用于焊缝缺陷检测的射线图像数据集

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-07 DOI: 10.1007/s10921-025-01186-w

Xuefeng Zhao, Juntao Wu, Baoxin Zhang, Haoyu Wen, Xiaopeng Wang, Yan Li, Xinghua Yu

引用次数: 0

A Review of Ultrasonic Testing and Evaluation Methods with Applications in Civil NDT/E 超声检测与评价方法及其在民用无损检测中的应用综述

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-07 DOI: 10.1007/s10921-025-01190-0

Inad Alqurashi, Ninel Alver, Ulas Bagci, Fikret Necati Catbas

{"title":"A Review of Ultrasonic Testing and Evaluation Methods with Applications in Civil NDT/E","authors":"Inad Alqurashi, Ninel Alver, Ulas Bagci, Fikret Necati Catbas","doi":"10.1007/s10921-025-01190-0","DOIUrl":"10.1007/s10921-025-01190-0","url":null,"abstract":"<div><p>New construction or existing structures are tested for material quality as well as damage and defects during their life cycle. Advancements in data science and sensor technologies have significantly improved Non-Destructive Testing (NDT) for monitoring the health of civil structures. This review covers 82 recent studies on ultrasonic testing (UT) methods used in civil NDT/E. It explains basic ultrasound principles, new developments in signal and image processing, and evaluates key imaging techniques like Synthetic Aperture Focusing Technique (SAFT), Total Focusing Method (TFM), and Computed Tomography (CT) for identifying defects and assessing materials. The review also discusses the use of advanced signal processing methods to better characterize defects and enhance image quality. Emerging trends include the use of machine learning for automatically identifying defects, new sensor technologies for real-time monitoring, and integrated monitoring systems using Internet of Things (IoT) and edge computing. Applications in material evaluation and testing structural components such as bridges and buildings are also examined. The review highlights challenges like handling large amounts of data, ensuring efficient computations, and creating standardized testing protocols. Future research directions aim to make UT methods more accurate, reliable, and scalable, contributing to the safety and durability of civil infrastructure. This review provides valuable information for researchers, engineers, and industry professionals on the latest developments, current challenges, and future opportunities in ultrasonic-based civil infrastructure NDT/E.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919268","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

Experimental Evaluation on Acoustic Emission Wave Propagation of Laminated Composites with Cutouts Using Auto Sensor Testing 基于自动传感器测试的带切口层合复合材料声发射波传播实验评价

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-07 DOI: 10.1007/s10921-025-01192-y

Binayak Bhandari, Gangadhara B. Prusty

{"title":"Experimental Evaluation on Acoustic Emission Wave Propagation of Laminated Composites with Cutouts Using Auto Sensor Testing","authors":"Binayak Bhandari, Gangadhara B. Prusty","doi":"10.1007/s10921-025-01192-y","DOIUrl":"10.1007/s10921-025-01192-y","url":null,"abstract":"<div><p>This study investigates Acoustic Emission (AE) wave propagation in large cantilever composite panels using experimental and numerical analysis. Five distinct case studies, considering two distinct scenarios—panels with and without cutouts—were investigated under various boundary conditions and excitations. The boundary conditions and support deviate from prevailing AE studies on flat panels supported by simply supported structures to focus on cantilever support that resembles aeroplane wings. A comparative study on the effectiveness of the Hanning Window Function, Sine, and square wave functions in characterizing wave propagation within laminated composite structures was investigated. Variations of the Hanning window function at frequencies of 100 kHz, 400 kHz, and 55 kHz are examined to assess their impact on stress wave time-of-arrival. Through experimental endeavours and three-dimensional (3D) numerical models, meticulous analyses on stress wave propagation time-of-arrival, frequencies, and excitation waveform were performed. Experiments were conducted using the Auto Sensor Testing (AST) feature for its superior repeatability and data acquisition over conventional methods such as pencil-break and impulse hammer tests. Results from Case I (without cutout) and Case III (with cutout) show that the wave propagation time from trigger sensor 1 to sensor 3 was 78.5 µs for the panel without a cutout and 125.5 µs for the panel with a cutout. This significant time discrepancy underscores the impact of boundary conditions and excitation waveforms on wave propagation in panels with and without cutouts. Comparative analysis affirms specific excitation waveform and frequency suitability, aligning numerical results with experimental observations, thereby substantiating the reliability and accuracy of the proposed numerical methodology.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01192-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918992","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

Multi-region Group Sampling Radius Semantic Segmentation Network Guided by Spatial Information for Highway 基于空间信息的高速公路多区域群采样半径语义分割网络

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-07 DOI: 10.1007/s10921-025-01188-8

Ming Guo, Xiaolan Zhang, Peng Cheng, Ming Huang, Liqiong Liao

{"title":"Multi-region Group Sampling Radius Semantic Segmentation Network Guided by Spatial Information for Highway","authors":"Ming Guo, Xiaolan Zhang, Peng Cheng, Ming Huang, Liqiong Liao","doi":"10.1007/s10921-025-01188-8","DOIUrl":"10.1007/s10921-025-01188-8","url":null,"abstract":"<div><p>High-precision road point cloud measurement using mobile LiDAR technology is essential digital infrastructure for various industries. Researchers focus primarily on developing high-precision automated semantic segmentation for road point clouds. Existing deep learning networks trained on uneven and sparse point clouds captured by self-developed Mobile LiDAR Systems (MLS) have low segmentation accuracy. This paper introduces a deep learning method that partitions data based on the spatial positions of road scene point clouds and considers the sampling radius of regional groups. We use a road point cloud dataset constructed with a self-developed MLS to train and test the semantic segmentation of road point clouds. Based on the linear characteristics of local road point clouds, Principal Component Analysis (PCA) and threshold filtering methods are applied to classify the point cloud into ground and non-ground points. Different sampling strategies are then employed for each class of points, which are subsequently fed into the network model for semantic segmentation. Experimental results show that the proposed method achieves an overall accuracy of 97.8% in road point cloud segmentation and a mean Intersection-Over-Union (mIOU) of 0.81. The specific mIOUs are 0.98 for roads, 0.98 for guardrails, 0.93 for signs, 0.96 for street lamps, and 0.56 for lane markings. These results indicate that the proposed method significantly improves the accuracy of segmenting uneven and sparse road point clouds captured by MLS and outperforms existing methods.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919031","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

Microstructural Analysis of Multi-Phase Coated Hot Stamped Steel Using Magnetic Barkhausen Noise 磁巴克豪森噪声对多相涂层热冲压钢组织的分析

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-02 DOI: 10.1007/s10921-025-01189-7

Aitor Lasaosa, Maialen Ramirez, Itsaso Artetxe, Kizkitza Gurruchaga, Ane Martinez-De-Guerenu

{"title":"Microstructural Analysis of Multi-Phase Coated Hot Stamped Steel Using Magnetic Barkhausen Noise","authors":"Aitor Lasaosa, Maialen Ramirez, Itsaso Artetxe, Kizkitza Gurruchaga, Ane Martinez-De-Guerenu","doi":"10.1007/s10921-025-01189-7","DOIUrl":"10.1007/s10921-025-01189-7","url":null,"abstract":"<div><p>The microstructure of multi-phase coated hot stamped steel samples is analyzed non-destructively using the magnetic Barkhausen noise (MBN) testing method. The analysis includes examining the microstructural phases in the bulk of hot stamped samples and estimating the thickness of the Al-Si diffusion layer (DL), which exhibits a ferritic microstructure. The MBN of three sets of hot stamped ultra-high strength steel sheets are analyzed. The first set of samples with the same DL is used to study the effect of the microstructural phases of the bulk material and to qualitatively assess the amount of the phases present, while the second and third sets, both with an almost fully martensitic bulk, are used to analyze the capacity of MBN to characterize DL thickness. The results demonstrate that the first peak of the MBN envelope at a very low magnetic field level is due to the superficial ferritic DL, while the peaks at higher levels reflect the microstructural phases of the hot stamped steel. In cases where the ferrite is predominant in the bulk, a peak emerges at medium magnetic field levels, whereas if martensite is the predominant phase, a peak is reached at a higher magnetic field. When both phases are present in significant amounts, both peaks can be observed. Moreover, the amplitude of the first peak derived from the MBN envelope could be used to determine DL thickness. Therefore, an analysis of the MBN envelope signal could provide useful information about the quality of the hot stamping process.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01189-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900738","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

Monitoring Dental Zinc Oxide Polyacrylate Cement Using Near-Infrared Spectroscopy 近红外光谱监测牙科氧化锌聚丙烯酸酯水泥

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-04-26 DOI: 10.1007/s10921-025-01185-x

Yuta Otsuka, Tadahiro Higashinakao, Hiroshi Kono, Masafumi Kikuchi

引用次数: 0

Efficient Defect Detection Method for Wire and Arc Additive Manufacturing Based on Modified YOLOv8 Model 基于改进YOLOv8模型的线材电弧增材制造缺陷高效检测方法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-04-25 DOI: 10.1007/s10921-025-01181-1

Yunli Huang, Xiangman Zhou, Xiaochen Xiong, Youheng Fu

{"title":"Efficient Defect Detection Method for Wire and Arc Additive Manufacturing Based on Modified YOLOv8 Model","authors":"Yunli Huang, Xiangman Zhou, Xiaochen Xiong, Youheng Fu","doi":"10.1007/s10921-025-01181-1","DOIUrl":"10.1007/s10921-025-01181-1","url":null,"abstract":"<div><p>Surface defect detection of parts manufactured by wire arc additive manufacturing (WAAM) is an important step for subsequent process improvement, optimization, and defect suppression. However, traditional methods and existing detection models suffer from high parameter counts, hardware requirements, and low accuracy. We presents a WAAM weld surface defect detection method derive from YOLOv8n, called high-efficiency new YOLO (HEN-YOLO). To address these limitations, a novel feature interaction detection head (NFIDH) is designed to enhance the feature learning and selectivity, reducing parameters and calculate losses. Subsequently, a lightweight and efficient local attention (ELA) mechanism was introduced to enhance both computational efficiency and detection accuracy of the model. Furthermore, the advanced screening feature fusion pyramid (HS-FPN) was employed to achieve cross-scale feature fusion and improve feature representation. Additionally, ConvTranspose2d deconvolution was utilized to optimize the upsampling process in the neck network, enabling the extraction of more effective and richer features. Finally, Experiments on 3440 WAAM weld surface defect dataset and the NEU-DET are maded to test the validity of HEN-YOLO. Results show that the mAP@.5(%) and mAP@.5:.95(%) of the HEN-YOLO are 2.4% and 8.3% higher than the YOLOv8n, respectively, which significantly improves the precision of weld surface defects detection; afterwards, it achieves a model parameters of 2.897 M and an 11.2% increase in FPS, surpassing the original YOLOv8n, which demonstrates that the HEN-YOLO has superior detection performance. This demonstrates that HEN-YOLO is efficient and can meet the practical detection requirements, and provides an efficient detection scheme for the weld defects in WAAM.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875305","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

Voids Imaging on Underwater Concrete Slabs Based on Guide Wave Transmission and Distributed Transducer Network 基于导波传输和分布式换能器网络的水下混凝土板空洞成像

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-04-14 DOI: 10.1007/s10921-025-01187-9

Zijian Wang, Kui Wang, Yuwei Yan, Zhangkai Peng, Zhishen Wu

{"title":"Voids Imaging on Underwater Concrete Slabs Based on Guide Wave Transmission and Distributed Transducer Network","authors":"Zijian Wang, Kui Wang, Yuwei Yan, Zhangkai Peng, Zhishen Wu","doi":"10.1007/s10921-025-01187-9","DOIUrl":"10.1007/s10921-025-01187-9","url":null,"abstract":"<div><p>For the construction and maintenance of bridges, dams, pipelines, etc., the damage detection method of underwater concrete components is increasingly important to ensure the safety and reliability of civil infrastructures. Current visual and sonar techniques are not robust and sensitive for inspecting underwater concrete components, especially in low illumination and muddy water. Therefore, this paper proposes an ultrasonic method to detect underwater concrete voids besides traditional methods. First, theoretical derivation reveals the wave modes propagating at the water-concrete interface as well as the wave velocities. Second, finite element simulation is used to investigate the wave transmission through a void. Rayleigh waves are more reliable and sensitive to characterize underwater voids than bulk and Scholte waves. Third, a damage index is formulated considering the energy attenuation of the transmission of Rayleigh waves. The interaction of abnormal wave paths indicates the void in a damage image. Finally, a distributed transducer network is developed in experiments to image the concrete slabs with single and double voids underwater. The proposed method can enrich the nondestructive testing of underwater structures and ensure the safety of civil infrastructures.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830875","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