Journal of Nondestructive Evaluation最新文献

Damage Classification of Carbon Fiber Reinforced Polymer Cables under Progressive Reciprocating Tensile Loading using a Hybrid Deep Learning Approach 基于混合深度学习的连续往复拉伸载荷下碳纤维增强聚合物电缆损伤分类

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-29 DOI: 10.1007/s10921-026-01371-5

Peng-Fei Zhang, Lian-Hua Ma, Ran Liu, Wei Zhou

{"title":"Damage Classification of Carbon Fiber Reinforced Polymer Cables under Progressive Reciprocating Tensile Loading using a Hybrid Deep Learning Approach","authors":"Peng-Fei Zhang, Lian-Hua Ma, Ran Liu, Wei Zhou","doi":"10.1007/s10921-026-01371-5","DOIUrl":"10.1007/s10921-026-01371-5","url":null,"abstract":"<div><p>Carbon fiber reinforced polymer (CFRP) cables are increasingly used in bridges, cableways, and other engineering structures due to their lightweight, high strength, and excellent corrosion resistance. However, accurately identifying and monitoring their damage modes under cyclic loading remains a critical challenge for structural health monitoring. In this study, progressive reciprocating tensile tests were conducted on CFRP cables and their constituent materials, while acoustic emission (AE) technique was employed to capture damage‑related signals. To achieve precise classification of damage modes, a Kepler Optimization Algorithm (KOA) enhanced k‑medoids clustering was first used to preliminarily identify the characteristic frequency ranges of matrix cracking and fiber breakage. Subsequently, a hybrid deep learning model integrating convolutional neural networks (CNN), bidirectional long short‑term memory networks (BiLSTM), and an attention mechanism, with hyperparameters optimized by KOA, was developed for accurate damage classification. The results demonstrate that the proposed KOA‑CNN‑BiLSTM‑Attention model achieves a classification accuracy exceeding 99.71% (validated by tenfold cross‑validation) and effectively distinguishes three damage modes: matrix cracking, debonding, and fiber fracture. The model’s predicted damage evolution shows excellent agreement with experimental observations of final failure behaviors, such as matrix explosion and fiber fracture. This work provides a reliable deep‑learning‑based tool for in‑situ damage monitoring of CFRP cables under service‑like cyclic loading.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797101","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

EHE-YOLO: An Enhanced Detection Approach for CFRP Hole-Making Burr Defects EHE-YOLO：一种增强的CFRP制孔毛刺缺陷检测方法

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-29 DOI: 10.1007/s10921-026-01365-3

Rongrong Wang, Jigang Wu, Youzhi Jiang, Tai An, Li Sun, Jiaming Chen

{"title":"EHE-YOLO: An Enhanced Detection Approach for CFRP Hole-Making Burr Defects","authors":"Rongrong Wang, Jigang Wu, Youzhi Jiang, Tai An, Li Sun, Jiaming Chen","doi":"10.1007/s10921-026-01365-3","DOIUrl":"10.1007/s10921-026-01365-3","url":null,"abstract":"<div><p>Carbon Fiber Reinforced Plastic (CFRP) is widely used in aerospace and other fields due to its high specific strength and high specific modulus. Hole-making is a critical process in the assembly of CFRP components. However, the anisotropy of CFRP often results in burr defects during this process, which compromise assembly precision and reduce service life. Therefore, accurate detection of burrs in CFRP hole-making is crucial for ensuring the safety of components. In response to the challenges of detecting burr defects in CFRP, such as severe background texture interference, blurred defect edges, a wide range of target scales, and difficulties in identifying small target defects, this paper proposes the Enhanced High-Extraction YOLO (EHE-YOLO) defect detection method. It introduces the Efficient Aggregation Enhanced Module (EAEM) to boost feature extraction against background and edge blurring, designs the High Resolution Boundary Enhanced Neck (HRBE-Neck) for cross-scale co-optimization, and constructs the Efficient Detail Capture Detection Head (EDCDH) for micro-burr recognition. Validated on the self-constructed CFRP-Burr dataset through ablation studies, comparative analyses, and visualization experiments, and validated through generalization experiments on the Severstal Steel Defect and VisDrone2019 datasets. The results demonstrate that EHE-YOLO achieves a detection accuracy of 91.51% for CFRP burrs, enabling high-precision detection of multi-scale and small targets. Finally, this paper discusses the advantages and limitations of the EHE-YOLO model, proposes targeted directions for further improvement, and summarizes the model’s value in industrial applications as well as its future development prospects.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797100","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

Ultrasonic Inspection of Tow-Steered Composite Laminates During Autoclave Cure 热压釜固化过程中双向复合材料层合板的超声检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-29 DOI: 10.1007/s10921-026-01366-2

Tyler B. Hudson, Cyrus J. R. Kosztowny, Nicholas A. Georgiou, Ryan S. Enos, Finnian P. Day

{"title":"Ultrasonic Inspection of Tow-Steered Composite Laminates During Autoclave Cure","authors":"Tyler B. Hudson, Cyrus J. R. Kosztowny, Nicholas A. Georgiou, Ryan S. Enos, Finnian P. Day","doi":"10.1007/s10921-026-01366-2","DOIUrl":"10.1007/s10921-026-01366-2","url":null,"abstract":"<div><p>Due to the anisotropic properties of unidirectional carbon fiber reinforced polymer (CFRP) composites, novel layup techniques, such as tow-steering, can be used to tailor mechanical properties of the composite structure. In this work, two tow-steered composite panels were designed, fabricated using the Integrated Structural Assembly of Advanced Composites (ISAAC) automated fiber placement (AFP) machine located at the NASA Langley Research Center (LaRC), and cured while simultaneously being inspected using an ultrasonic inspection system operating inside the autoclave. A pristine panel was fabricated along with a panel with intentionally placed defects. The defect composite panel contained overlaps that are intrinsic to the tow-steered design and had intentionally introduced layup defects including folds, wrinkles, splices, tow twists, foreign object debris (FOD), gaps, and additional overlaps. The inspections during the cure cycle focused on the area within the “defect” laminate containing a fold, a twist, and intrinsic overlaps. The ultrasonic inspections performed during the cure cycle were analyzed and compared to post-cure ultrasonic inspections of both laminates. An ultrasound method and a structured light method were used to quantify warpage of each panel post cure.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797120","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

Hybrid Deep Learning-Based Automatic Identification, Localization and Quantitative Evaluation of Internal Defects in Welds 基于混合深度学习的焊缝内部缺陷自动识别、定位与定量评估

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-29 DOI: 10.1007/s10921-026-01368-0

Qi Zheng, Hao Wang, Xiaohui Zhao, Xiujun Wang, Mengran Li, Chao Chen

{"title":"Hybrid Deep Learning-Based Automatic Identification, Localization and Quantitative Evaluation of Internal Defects in Welds","authors":"Qi Zheng, Hao Wang, Xiaohui Zhao, Xiujun Wang, Mengran Li, Chao Chen","doi":"10.1007/s10921-026-01368-0","DOIUrl":"10.1007/s10921-026-01368-0","url":null,"abstract":"<div><p>Currently, there is a lack of integrated application solutions for the rapid identification, localization, and quantitative evaluation of internal defects in weld seams within industrial scenarios. Based on this, this study proposes a hybrid detection scheme that integrates You Only Look Once version 8 (YOLOv8) with the Cascade Feedforward Neural Network (CFNN), establishing an intelligent technical system that encompasses the entire workflow of defect quality inspection. In this scheme, YOLOv8 utilizes a lightweight feature pyramid network to enable accurate identification of defects; CFNN constructs a nonlinear mapping model between the pixel coordinates of defect features and their corresponding physical coordinates to achieve accurate localization; and a novel geometric integration approach is proposed to calculate the cross-sectional area of defect contours, thereby facilitating quantitative defect evaluation. The verification experimental results indicate that the defect recognition accuracy achieves 100%. The average errors for the lateral and vertical coordinates of the defects (<span>({text{W}}_{1}^{*})</span>,<span>({text{W}}_{2}^{*})</span>) are 0.000332 mm (at the sixth decimal place) and 0.000576 mm (at the sixth decimal place), respectively, while the average error for the depth coordinate (<span>({text{h}}^{*})</span>) is 0.001577 mm. In the process of quantitative evaluation of defects, the average errors in the height (<span>({text{H}}^{*})</span>) and cross-sectional area (<span>({text{S}}^{*})</span>) of weld defects are 0.00282 mm and 1.46 mm<sup>2</sup>, respectively. This study presents a comprehensive and integrated technical solution for the online detection of weld defects in industrial environments, which significantly advances the practical implementation of ultrasonic testing technology in actual industrial applications.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797139","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

Development of Inspection Method for Steel structure Using Active Temperature Gap Method with Backside Heating 主动温度间隙法后加热钢结构检测方法的发展

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-29 DOI: 10.1007/s10921-026-01369-z

Daisuke Imanishi, Daiki Shiozawa, Takahide Sakagami

{"title":"Development of Inspection Method for Steel structure Using Active Temperature Gap Method with Backside Heating","authors":"Daisuke Imanishi, Daiki Shiozawa, Takahide Sakagami","doi":"10.1007/s10921-026-01369-z","DOIUrl":"10.1007/s10921-026-01369-z","url":null,"abstract":"<div><p>This study proposes a novel non-contact inspection technique for detecting fatigue cracks in steel structures, specifically targeting the travelling girders of grab-type unloader cranes. These girders are often difficult to inspect due to their height and limited accessibility. The proposed method, called the Active Temperature Gap Method with backside heating, utilizes localized heating on the internal surface of the girder to induce a temperature gap at crack locations, which is then visualized using infrared thermography. Laboratory experiments and finite element (FE) analysis were conducted to determine optimal heating conditions, including heating time and distance. A heat gun was used as a practical heating source, and the effectiveness of both fixed and travelling heating methods was evaluated. The results showed that a heating distance of 30 mm and a heating time of 20 s provided the best balance between thermal diffusion and crack insulation. Additionally, the use of a travelling heat source significantly improved the uniformity of heating and enhanced crack edge detection. Image processing using the Sobel filter further improved the clarity and accuracy of crack visualization. The method successfully detected both surface and through thickness cracks, even in areas inaccessible from the outside. This approach offers a promising solution for efficient, safe, and cost-effective maintenance of aging industrial infrastructure. Future work will focus on field validation and automation of the heating and image analysis processes to support real time inspection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797122","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

Feature Selection of Multi-Channel Acoustic Signals Based on Weighted KL Distance for Boiler Tube Leakage Detection 基于加权KL距离的多通道声信号特征选择锅炉管泄漏检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-28 DOI: 10.1007/s10921-026-01370-6

Chao Wang, Yaran Wang, Qiuyu Wang, Da Liu, Hao Liu

{"title":"Feature Selection of Multi-Channel Acoustic Signals Based on Weighted KL Distance for Boiler Tube Leakage Detection","authors":"Chao Wang, Yaran Wang, Qiuyu Wang, Da Liu, Hao Liu","doi":"10.1007/s10921-026-01370-6","DOIUrl":"10.1007/s10921-026-01370-6","url":null,"abstract":"<div><p>The safe and stable operation of power plants is seriously threatened by boiler tube leakage accidents, making accurate detection of boiler tube leakages essential. However, minor leakages are difficult to detect under the influence of strong hot-state background noise in boilers, and the variety and quantity of available leakage acoustic signals on site are very limited. An equivalent criterion based on sound pressure amplitude is derived, and a method based on the proportional relationship of effective sound pressure is employed to map the hot-state background noise to the experimental environment, thereby constructing a leakage acoustic detection platform that is almost equivalent to the boiler environment. On the platform, various types of leakage acoustic signals with noise are collected, effectively addressing the issue of sample scarcity. To fully utilize the information from multi-channel acoustic sensors and enhance the detection accuracy of minor leakages, a calculation method based on weighted Kullback–Leibler (KL) distance is proposed to select the time-domain and frequency-domain statistical features and wavelet packet energy (WPE) features from four-channel acoustic signals. Experimental results show that the proposed feature selection method combined with a support vector machine (SVM) outperforms the detection method using an acoustic signal energy threshold, with a detection accuracy of 97.00% versus 65.36%. Furthermore, when detecting acoustic data from leakage source locations that were not involved in the training dataset, as well as data collected from the cold-state boiler environment, the detection accuracies of the proposed method are 92.67% and 93.53%, with the narrowest confidence intervals. The results demonstrate that the proposed method exhibits superior generalization capability compared to the feature selection method based on Relief-F and KL distance.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797061","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

Study on Directivity and Propagation Path of Laser Ultrasound under Inhomogeneous Temperature in Nickel-based Superalloy 非均匀温度下激光超声在镍基高温合金中的方向性及传播路径研究

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-28 DOI: 10.1007/s10921-026-01364-4

Yan Tang, Hai Gong, Tao Zhang, Peng Cheng

{"title":"Study on Directivity and Propagation Path of Laser Ultrasound under Inhomogeneous Temperature in Nickel-based Superalloy","authors":"Yan Tang, Hai Gong, Tao Zhang, Peng Cheng","doi":"10.1007/s10921-026-01364-4","DOIUrl":"10.1007/s10921-026-01364-4","url":null,"abstract":"<div><p>Superalloys are widely used in aerospace for their high tensile and yield strength under high temperatures. Laser ultrasonic technology (LUT) is the most promising means to achieve online real-time detection in extreme environments. The directivity and propagation path of laser-generated ultrasound are affected by inhomogeneous temperature fields. To accurately obtain detection signals in high temperatures, it is necessary to study the influence of inhomogeneous temperature fields on the directivity and propagation path of laser ultrasound (LU). In this paper, the directivity and amplitude of LU in the nickel-based superalloy are investigated by theoretical analysis under different temperatures, and the propagation paths of longitudinal (L) waves generated in the thermoelastic regime and shear (S) waves generated in the ablation regime are analyzed in inhomogeneous temperature fields by the developed procedure. It is shown that the amplitude of the L wave decreased by 22%, and the relative position variation <span>({Delta beta }_{TL})</span> exceeded by 17%. Numerical simulations and experiments are conducted to study and verify the directivity, amplitude, and propagation paths of the LU under inhomogeneous temperature. The simulation results show that the amplitude of the L wave reduced by 17% in the thermoelastic regime, and the optimal receiving position decreases as the temperature gradients increase. The experiment results also show that the optimal receiving position decreases with rising temperature gradients, and verify the accuracy of the developed procedure and simulation models. The relationships between the temperature gradient and relative position changes are established in different temperature ranges. This work establishes a reference and theoretical basis for the application of LUT in high-temperature detection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796800","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

Numerical Investigation on the X-ray Imaging Inspection for Grout Loss Evaluation of Bridges 桥梁灌浆损失评价的x射线成像检测数值研究

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-20 DOI: 10.1007/s10921-026-01357-3

Zhenjun Li, Hengxi Chen, Akio Sugita, Masahiro Abe, Shuichi Hasegawa

{"title":"Numerical Investigation on the X-ray Imaging Inspection for Grout Loss Evaluation of Bridges","authors":"Zhenjun Li, Hengxi Chen, Akio Sugita, Masahiro Abe, Shuichi Hasegawa","doi":"10.1007/s10921-026-01357-3","DOIUrl":"10.1007/s10921-026-01357-3","url":null,"abstract":"<div><p>The X-ray imaging technique has evolved into one of the most effective inspection methods for evaluating internal degradation of bridges. For the inspection of grout loss in bridge evaluation, a numerical model was established to analyze its detectability in the X-ray non-destructive inspection. Different cases were developed by adjusting the rod radius and grout fraction inside the sheath in terms of the rod-to-sheath structure. The simulated projection revealed that the gray value difference of the healthy side Z<sub>G</sub> is lower than that of the air mixed side Z<sub>A+G</sub>, which agrees well with the lower attenuation coefficient of air compared with grout. A relative ratio R<sub>G</sub> was further defined to evaluate the detectability of each structure. Besides, small rod radius and large loss ratio led to significant differences between healthy and air mixed parts, indicating a better detectability of the structure. Additionally, the hypothesis testing was used to determine whether the structure was successfully detected based on the given p-value. The failures of the detections demonstrate the limitation of the X-ray source in inspecting tiny structures. Overall, the study is helpful in guiding the on-site bridge inspection and evaluation using X-rays theoretically.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738009","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

Automatic Deep Anomaly Detection using Thermograpy based Convolution Autoencoder Framework 基于热像仪的卷积自编码器框架自动深度异常检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-17 DOI: 10.1007/s10921-026-01360-8

Naga Prasanthi Yerneni, L. Sainath, V. S. Ghali, Sk. Aashik, G. T. Vesala, V. Dhanunjana Chari, Fei Wang

{"title":"Automatic Deep Anomaly Detection using Thermograpy based Convolution Autoencoder Framework","authors":"Naga Prasanthi Yerneni, L. Sainath, V. S. Ghali, Sk. Aashik, G. T. Vesala, V. Dhanunjana Chari, Fei Wang","doi":"10.1007/s10921-026-01360-8","DOIUrl":"10.1007/s10921-026-01360-8","url":null,"abstract":"<div><p>Metals and composite structures are widely used in various industries due to their high mechanical strength and durability. However, defects generated during the manufacturing and operating phase limit their future usefulness and are recommended through non-invasive inspection. Quadratic frequency-modulated thermography (QFMT) is a non-destructive testing technique applicable to many materials due to its high-energy deposition at lower frequencies for enhanced defect signatures. The recent past in QFMT is advanced with machine learning and deep learning-based techniques. In contrast, the highly class-imbalanced and scarce nature of thermal profiles has recently gained interest in anomaly detection models. A stacked denoising convolution autoencoder (SDCAE) driven Local Outlier Factor (LOF) is proposed in the present article to identify defects in mild steel and carbon fiber reinforced polymer specimens inspected by QFMT. Deep features extracted from the temporal thermal profiles using pre-trained SDCAE are further fed to LOF for automatic defect detection. A quantitative comparison with recently introduced deep anomaly detection models and other autoencoder models for performance analysis strengthens the suitability of proposed method for automatic defect detection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737691","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

Multi-Modal Source Separation for Electrical Impedance Tomography 电阻抗断层成像的多模态源分离

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-17 DOI: 10.1007/s10921-026-01363-5

Laura Homa, Mathew Schey, John Wertz

引用次数: 0