Journal of Nondestructive Evaluation最新文献_第4页

Utilizing a Four-Concave Capacitance Sensor and ANN to Forecast Void Fraction in Two-Phase Stratified Flow Independent of Liquid Type 利用四凹电容传感器和人工神经网络预测两相分层流中与液体类型无关的空隙率

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-09 DOI: 10.1007/s10921-025-01164-2

Mohammad Hossein Shahsavari, Seyed Mehdi Alizadeh, Evgeniya Ilyinichna Gorelkina, Umer Hameed Shah, John William Grimaldo Guerrero, Gholam Hossein Roshani, Ahmed Imran

{"title":"Utilizing a Four-Concave Capacitance Sensor and ANN to Forecast Void Fraction in Two-Phase Stratified Flow Independent of Liquid Type","authors":"Mohammad Hossein Shahsavari, Seyed Mehdi Alizadeh, Evgeniya Ilyinichna Gorelkina, Umer Hameed Shah, John William Grimaldo Guerrero, Gholam Hossein Roshani, Ahmed Imran","doi":"10.1007/s10921-025-01164-2","DOIUrl":"10.1007/s10921-025-01164-2","url":null,"abstract":"<div><p>The determination of void fraction in various two-phase flows holds great significance across a range of industries, including gas, oil, chemical, and petrochemical sectors. Scientists have proposed a wide array of methods for measuring void fractions. In comparison to other methods, capacitive-based sensors stand out as a good choice due to their affordability, nondestructively, robustness, and reliability. However, one of the factors that can affect the accuracy of these sensors is changes in the fluid composition. For instance, even a minor alteration in the fluid within the pipe can result in a significant void fraction measurement error. To address this issue, regular calibration is necessary, which can be a laborious task. In this paper, an Artificial Neural Network (ANN) is employed in order to make sensor measurements independent of fluid changes, which allows for more reliable and precise measurements without the need for frequent calibration. Our focus is on studying stratified two-phase flow. In this research, four different combinations of electrodes of a four-concave sensor are utilized as the input of an ANN. As a result, the ANN’s output accurately quantifies the void fraction. COMSOL Multiphysics software is utilized to simulate the behavior and measure the capacitance value of different combinations of this sensor. Additionally, a Multilayer Perceptron (MLP) neural network in MATLAB is designed and implemented, which can forecast the gas percentage within a two-phase fluid containing different liquids, achieving a remarkable mean absolute error of only 0.0031.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370032","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 the Distribution Patterns of Temperature Fields and Thermal Image Feature Enhancement in Tunnel Lining Cracks and Leakage 隧道衬砌裂缝与渗漏温度场分布规律及热图像特征增强研究

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-09 DOI: 10.1007/s10921-025-01162-4

Wang Kang, Lv Gaohang, Han Bo, Zhang Hanming, Liu Jian

{"title":"Study on the Distribution Patterns of Temperature Fields and Thermal Image Feature Enhancement in Tunnel Lining Cracks and Leakage","authors":"Wang Kang, Lv Gaohang, Han Bo, Zhang Hanming, Liu Jian","doi":"10.1007/s10921-025-01162-4","DOIUrl":"10.1007/s10921-025-01162-4","url":null,"abstract":"<div><p>Long-term operation of tunnels is influenced by factors such as material degradation and variations in service loads, resulting in varying degrees of lining cracks and water leakage. Addressing the challenge of low target contrast and significant background interference in thermal imaging detection of tunnel lining cracks, this study established an experimental system capable of simulating tunnel operational humidity and temperature environments. The lining cracks were localized and the temperature field distribution on the cracked lining surface was characterized by computer vision techniques. Additionally, numerical simulations using thermal-flow coupling were employed to validate and extend the results of indoor experiments. Based on the distribution patterns of the temperature field, a gain transformation function for thermal images was established. The findings indicate that the processed images were 2–6 times better than the conventional algorithm in terms of SCRG (Signal Clutter Ratio Gain) and BSF (Background Suppression Factor) metrics. This research provides valuable insights and references for the practical application of thermal imaging detection in tunnel water leakage scenarios.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369986","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

Particle Filter-Based Fault Prognosis of Live XLPE Insulated Aerial Bundled Cables Installed at Coastal Regions Using Historical Infrared Thermography Data 基于粒子滤波的沿海地区架空交联聚乙烯绝缘电缆历史红外热像仪故障预测

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-09 DOI: 10.1007/s10921-024-01152-y

Moez ul Hassan, Tariq Mairaj Rasool Khan, Waleed Bin Yousuf, Aqueel Shah

{"title":"Particle Filter-Based Fault Prognosis of Live XLPE Insulated Aerial Bundled Cables Installed at Coastal Regions Using Historical Infrared Thermography Data","authors":"Moez ul Hassan, Tariq Mairaj Rasool Khan, Waleed Bin Yousuf, Aqueel Shah","doi":"10.1007/s10921-024-01152-y","DOIUrl":"10.1007/s10921-024-01152-y","url":null,"abstract":"<div><p>Accelerated ageing of cross-linked polyethylene (XLPE) insulated aerial bundled cables (ABC) installed at coastal regions for electric power distribution is a topic of major concern. Sudden failures caused by rapid insulation deterioration initiate unexpected power shutdowns. Accurate prognosis of incipient failures in cables will enable maintenance agencies to plan repair or replacement activities well on time thus improving the reliability and availability of the transmission grid. In this research work, historical non-destructive evaluation (NDE) data was generated using infrared thermography of ABCs to ascertain cable degradation parameter. Historical NDE data embodies the progressive degradation experienced by the ABCs installed at harsh marine environment. The degradation growth in cable insulation, when subjected to rough environmental conditions, is non-linear coupled with non-Gaussian / multimodal noise distributions. Therefore, a renowned nonlinear Bayesian estimator namely Particle Filter (PF) is applied on the historical database to determine degradation growth evolution over time. The proposed framework is further complimented with f-step prediction scheme in future time to quantify the prediction accuracy of cable degradation growth in future where measurement data is not available. The so predicted results are then compared to the actual degradation in future. The prediction accuracy demonstrates the efficacy of proposed technique for prognosis of ABCs installed at different locations. Accurate state prediction in different life phases of ABCs further displays robustness of proposed technique in estimating actual degradation growth during the different life stages.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01152-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370014","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

An End-to-End Quantitative Identification Method for Mining Wire Rope Damage Based on Time Series Classification and Deep Learning 基于时间序列分类和深度学习的矿用钢丝绳损伤端到端定量识别方法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-09 DOI: 10.1007/s10921-025-01166-0

Chun Zhao, Jie Tian, Hongyao Wang, Zhangwen Shi, Xingjun Wang, Jingwen Huang, Lingguo Tang

{"title":"An End-to-End Quantitative Identification Method for Mining Wire Rope Damage Based on Time Series Classification and Deep Learning","authors":"Chun Zhao, Jie Tian, Hongyao Wang, Zhangwen Shi, Xingjun Wang, Jingwen Huang, Lingguo Tang","doi":"10.1007/s10921-025-01166-0","DOIUrl":"10.1007/s10921-025-01166-0","url":null,"abstract":"<div><p>Mining wire rope (MWR) is an important part of mine hoisting equipment and plays a key role in mining operations. Damage to these ropes can significantly reduce production efficiency and pose serious safety risks to workers. Therefore, quantitatively identifying damage in MWR is of great importance. Traditional methods for damage signal identification rely on manual feature extraction (MFE), which depends heavily on experience and lacks stability and flexibility. This paper proposes an end-to-end (E2E) quantitative identification model for MWR damage based on time series classification (TSC) and deep learning (DL). Unlike traditional methods, the E2E model learns features directly from the one-dimensional raw signals of MWR damage and does not require MFE. In order to test its validity and versatility, experiments were conducted on three different datasets. The results show that the E2E method performs well in quantitatively identifying MWR damage compared to other methods and this method meets the requirements of the mining industry in terms of precision and efficiency to ensure safe and reliable operation of mining work.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369987","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

Weakly Supervised Complex Texture Defect Detection Based on Nested U-Net Architecture 基于嵌套U-Net结构的弱监督复杂纹理缺陷检测

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-09 DOI: 10.1007/s10921-025-01161-5

Kuidong Huang, Yang Zeng, Julong Zhao, Shijie Chai, Fuqiang Yang

{"title":"Weakly Supervised Complex Texture Defect Detection Based on Nested U-Net Architecture","authors":"Kuidong Huang, Yang Zeng, Julong Zhao, Shijie Chai, Fuqiang Yang","doi":"10.1007/s10921-025-01161-5","DOIUrl":"10.1007/s10921-025-01161-5","url":null,"abstract":"<div><p>The rich and complex texture information of industrial products, and the fact that the number of normal products often far exceeds the number of defective products in industrial scenarios, poses a great challenge to product quality inspection. In order to solve this challenge, a weakly supervised defect detection model based on nested U-Net was proposed: the nested U-Net was used as the main body of the model, and an attention module was introduced, which could obtain the relationship between local features and between feature channels. Furthermore, A weakly supervised training strategy was employed: the defect mask from the Berlin noise, external data and normal samples are used to synthesize the defects, and a few real defect samples are randomly inserted into the synthetic defect samples to train the detection model. Experimental validation was carried out on the public datasets MVTec AD, DAGM, MT and custom CT (computed tomography) composite material dataset, and the evaluation indicators included image-level AUC (area under the receiver’s operating characteristic curve), pixel-level AUC and AP (average accuracy). Experimental results show that the proposed method achieves excellent performance of 99.9%/98.7%/84.1%, 99.1%/95.3%/76.1%, 100%/98.1%/86.7% and 73.6%/69.1%/36.0% on three types of metrics on four datasets, respectively, which is better than the current advanced model.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370049","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

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-09 DOI: 10.1007/s10921-025-01163-3

Mingyu Gao, Liang Huo, Fei Wang, Peng Song, Yulong Gao, Guohui Yang, Junyan Liu, Zhipeng Liang, Yunji Xie, Yinghao Song

{"title":"CNN-Based Similar Microwave Reflection Signals for Improved Detectability and Intelligent Characterization of Internal Defects in Composite Materials","authors":"Mingyu Gao, Liang Huo, Fei Wang, Peng Song, Yulong Gao, Guohui Yang, Junyan Liu, Zhipeng Liang, Yunji Xie, Yinghao Song","doi":"10.1007/s10921-025-01163-3","DOIUrl":"10.1007/s10921-025-01163-3","url":null,"abstract":"<div><p>Near-field microwave imaging shows considerable promise for non-destructive evaluation of internal defects in high silica/phenolic composites, which are commonly used as thermal protection systems (TPS) for rocket/missile solid motor nozzles and space re-entry vehicles. However, effectively identifying defect features using post-processing algorithms remains challenging. To address this challenge, this paper proposes a microwave defect characterization algorithm based on Convolutional Neural Networks (CNN). A defect dataset derived from reflection microwave signals, was manually compiled by detecting samples with critical defects. The CNN framework was utilized for precise classification of microwave signals, employing a classification encoding strategy to extract two-dimensional defect information and achieve automatic localization and imaging of defects. Multiple deep learning models were compared in both simulations and experiments, revealing that the proposed CNN exhibited significant advantages in feature extraction, enabling highly effective identification of internal defects even with a limited dataset. Compared with traditional algorithms, the detection accuracy of the proposed 1D-SENet has been improved by 53.35% and 50.66%, respectively, and can achieve detection of defects with a minimum size of Φ6mm. These validate the effectiveness of algorithm in intelligent and automated microwave characterization of delamination defects within composite materials.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370019","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

High Quality Ultrasonic Imaging at Low Detection Frequency for Defects in Thick Composites 厚复合材料缺陷的低检测频率高质量超声成像

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-07 DOI: 10.1007/s10921-024-01155-9

Hui Zhang, Min Zhang, Haiyan Zhang, Yiting Chen, Wenfa Zhu, Qi Zhu

引用次数: 0

Spreading and Shrinking Effects of Coplanar Capacitive Sensors for Surface Defects 表面缺陷共面电容式传感器的扩展和收缩效应

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-07 DOI: 10.1007/s10921-025-01160-6

M. Mwelango, X. Yin, M. Zhao, R. Fan, Z. Han, G. Fan, P. Ma, X. Yuan, W. Li

{"title":"Spreading and Shrinking Effects of Coplanar Capacitive Sensors for Surface Defects","authors":"M. Mwelango, X. Yin, M. Zhao, R. Fan, Z. Han, G. Fan, P. Ma, X. Yuan, W. Li","doi":"10.1007/s10921-025-01160-6","DOIUrl":"10.1007/s10921-025-01160-6","url":null,"abstract":"<div><p>With increasing attention to safety, accurate detection of defects in diverse materials is crucial. Non-Destructive Evaluation (NDE) techniques play a key role in this effort. However, many approaches overlook preliminary signal analysis, which is vital for understanding fundamental sensor signal characteristics and improving advanced techniques. This study aims to investigate the fundamental signal properties from coplanar capacitive sensors (CCS) with square electrodes to gain insights into the relationship between signal properties and actual linear defect size in both conducting and non-conducting materials. The first-order derivatives (1st O.DE) of raw signals obtained from CCS through both simulations and experiments were further analysed, focusing on a circular surface defect. Nine sensor configurations were tested to examine the spreading effect. Experiment and simulation results were in good agreement, showing that the Full Width at Zero (FWZ) of the raw signals of both materials is greater than the actual defect diameter (spreading effect) whereas the signals processed using the 1st O.DE exhibited peak-trough widths greater than the actual defect diameter in non-conducting materials (spreading effect) and less than the actual diameter in conducting materials (shrinking effect). These results underscore that the spreading and shrinking effects are intrinsic characteristics of the CCS, attributed to the behavior of the CCS’s electric field and sensitivity distribution field (SDF) when interacting with different materials. By incorporating these insights into novel and advanced methods—such as imaging algorithms, machine learning approaches, and data fusion techniques—future developments can be effectively guided to enhance the accuracy, reliability, and advancement of defect detection, imaging, and sizing in coplanar capacitive sensing for NDE.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361639","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

Effect of a Crack on the Displacement Current Field of Non–electrically Conductive Materials via Electromagnetic Induction Testing 电磁感应试验研究裂纹对非导电材料位移电流场的影响

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-05 DOI: 10.1007/s10921-024-01151-z

Wataru Matsunaga, Yoshihiro Mizutani

{"title":"Effect of a Crack on the Displacement Current Field of Non–electrically Conductive Materials via Electromagnetic Induction Testing","authors":"Wataru Matsunaga, Yoshihiro Mizutani","doi":"10.1007/s10921-024-01151-z","DOIUrl":"10.1007/s10921-024-01151-z","url":null,"abstract":"<div><p>The range of application of eddy current testing (ECT) has been recently extended to non-electrically conductive materials, in which case it is called electromagnetic induction testing (EIT) given that EIT detects changes in the electromagnetic field of the displacement current. Although EIT has been reported for non-destructive characterization of non-electrically conductive materials, its detection principle is still unclear. We used finite element analysis (FEA) and experiments to evaluate the effect of cracks on the displacement current field in non-electrically conductive materials for crack detection using EIT. FEA was performed on electrically and non-electrically conductive materials with slits simulating cracks. The FEA results showed that crack detection differed between materials, as eddy currents bypassed the crack, while displacement currents passed through and formed a current path. Furthermore, the electric field intensity and displacement current induced in the non-electrically conductive materials varied significantly in the cracked area compared with the uncracked area. Experiments were conducted to detect cracks in carbon fiber reinforced thermoplastics (CFRTPs) and glass fiber reinforced plastics (GFRPs), which have isotropic electrical properties in the in-plane direction. In the CFRTP, the electromagnetic field varied significantly even at locations far from the crack, whereas it changed only slightly near the crack in the GFRP. This result demonstrates that for non-electrically conductive materials, EIT can identify cracks by detecting localized changes in the displacement current flowing through the cracks. Our findings can help clarify the principle of crack detection in non-electrically conductive materials, thereby extending the application of EIT to these materials.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184612","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

Developing a Neural Network Based Microwave Sensing System for Accurate Salinity Prediction in Water 基于神经网络的水中盐度微波传感系统的研制

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-02-04 DOI: 10.1007/s10921-024-01156-8

Muhammed Ismail Pence, Cemanur Aydinalp, Semih Doğu, Mehmet Nuri Akıncı

{"title":"Developing a Neural Network Based Microwave Sensing System for Accurate Salinity Prediction in Water","authors":"Muhammed Ismail Pence, Cemanur Aydinalp, Semih Doğu, Mehmet Nuri Akıncı","doi":"10.1007/s10921-024-01156-8","DOIUrl":"10.1007/s10921-024-01156-8","url":null,"abstract":"<div><p>High and low salinity levels play a crucial role in the vitality of organisms and affect natural ecosystems, agricultural yields and human health. To mitigate the risks associated with high blood pressure and cardiovascular diseases, the World Health Organization (WHO) advocates reducing salt consumption among adults, suggesting an intake of no more than 5 g daily. In this study, a non-invasive microwave (MW) sensing approach, that is augmented by deep neural network (DNN) models is proposed to predict salinity levels. The MW detection measurement system, including a Horn antenna, has been developed to evaluate the salt content in bottled spring waters (BSWs). The system with DNN model provides a novel solution for real-time water quality monitoring. The input and output dataset for DNN model were generated using four different BSWs, each with a salt content ranging from 0 to 32 g and increased by 1 g. The developed DNN model, designed with six fully connected layers, uses reflection coefficients (RCs) as input dataset to predict salt content in grams accurately. The accuracy performance of the DNN model in various bandwidths was evaluated by dividing the 1–13 GHz range into 78 different bands and the lowest error rate was found to be in the 1–8 GHz bandwidth (2.18%). Furthermore, each BSW was measured five times, and the performance of the model was evaluated according to the number of measurements. In three or more measurements, the model demonstrated notable improvement(15.3%) in predicting salt content.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01156-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107896","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