Journal of Nondestructive Evaluation最新文献_第6页

Statistical and Machine Learning-Based Imaging with Long Pulse Thermography for the Detection of Non-standardised Defects in CFRP Composites 基于统计和机器学习的长脉冲热成像CFRP复合材料非标准化缺陷检测

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-12-04 DOI: 10.1007/s10921-024-01138-w

Afonso Espirito Santo, Weeliam Khor, Francesco Ciampa

{"title":"Statistical and Machine Learning-Based Imaging with Long Pulse Thermography for the Detection of Non-standardised Defects in CFRP Composites","authors":"Afonso Espirito Santo, Weeliam Khor, Francesco Ciampa","doi":"10.1007/s10921-024-01138-w","DOIUrl":"10.1007/s10921-024-01138-w","url":null,"abstract":"<div><p>In the last few years, infrared long pulse thermography (LPT) has attained high reliability and accuracy in the non-destructive inspection of low-thermally conductive materials such as carbon fibre reinforced polymer (CFRP) composites. However, to date, research investigations of LPT have been conducted on standardised and controlled material flaws such as flat bottom holes. Non-standardised defects in CFRPs are more common in real-life operations and, because of different nature, dimensions and complex shapes, their detection poses a significant challenge. This paper provides an in-depth analysis of LPT combined to advanced statistical and machine learning-based image processing tools for detection of non-standardised damage in CFRP composites. Statistical methods such as skewness and kurtosis, and machine learning algorithms such as principal component analysis and Fuzzy-c clustering were used to post-process thermal LPT signals. Damage scenarios that are likely to occur during manufacturing and in-service operations were analysed in terms of defect mapping characteristics using the signal-to-noise ratio and the Tanimoto criterion. Experimental results revealed that Fuzzy-c and LPT produced superior damage inspection performance.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778295","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

Nondestructive Identification of Wheat Species using Deep Convolutional Networks with Oversampling Strategies on Near-Infrared Hyperspectral Imagery 基于过采样深度卷积网络的近红外高光谱图像小麦品种无损识别

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-12-04 DOI: 10.1007/s10921-024-01143-z

Nitin Tyagi, Sarvagya Porwal, Pradeep Singh, Balasubramanian Raman, Neerja Garg

{"title":"Nondestructive Identification of Wheat Species using Deep Convolutional Networks with Oversampling Strategies on Near-Infrared Hyperspectral Imagery","authors":"Nitin Tyagi, Sarvagya Porwal, Pradeep Singh, Balasubramanian Raman, Neerja Garg","doi":"10.1007/s10921-024-01143-z","DOIUrl":"10.1007/s10921-024-01143-z","url":null,"abstract":"<div><p>Differentiating between wheat species poses a significant challenge to the Indian grain industry. Visual inspection of wheat species has drawbacks, including inconsistency, low throughput, and labor intensiveness. In this study, near-infrared hyperspectral imaging (NIR-HSI) was utilized in conjunction with a deep learning approach to achieve precise predictions of wheat at the species level. A dataset comprising 40 different varieties from four Indian wheat species, namely <i>Triticum aestivum</i> (<i>T. aestivum</i>), <i>Triticum durum</i> (<i>T. durum</i>), <i>Triticum dicocccum</i> (<i>T. dicoccum</i>), and <i>Triticale</i>, was prepared using a NIR-HSI system that encompassed the wavelength ranging from 900–1700 nm. The imbalanced dataset is a common problem in the classification task, making it harder for the classifier to classify minority class data correctly. To address this issue, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic sampling (ADASYN) were employed. For the classification task, a 1D Convolutional Neural Network (1D-CNN), a 1D-ResNet, and four traditional machine learning models: Naive Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are utilized and compared. The performance of these models was assessed using both imbalanced and balanced datasets. The 1D-CNN model outperformed traditional machine learning models, achieving impressive test accuracies of 98.25% and 98.43% with the SMOTE and ADASYN approach, respectively. These findings underscore the efficacy of NIR-HSI in conjunction with an end-to-end 1D-CNN and oversampling techniques as a reliable and efficient method for the rapid, accurate, and nondestructive identification of various wheat species. The code is available at https://github.com/nitintyagi007-iitr/Wheat_species_classification</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778296","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

Nondestructive Evaluation of Adhesive Joints Using Nonlinear Non-collinear Wave Mixing Technique 基于非线性非共线波混频技术的粘接接头无损评价

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-12-04 DOI: 10.1007/s10921-024-01142-0

Jingpin Jiao, Zhiqiang Li, Li Li, Guanghai Li, Xinyuan Lu

{"title":"Nondestructive Evaluation of Adhesive Joints Using Nonlinear Non-collinear Wave Mixing Technique","authors":"Jingpin Jiao, Zhiqiang Li, Li Li, Guanghai Li, Xinyuan Lu","doi":"10.1007/s10921-024-01142-0","DOIUrl":"10.1007/s10921-024-01142-0","url":null,"abstract":"<div><p>Adhesive joints are extensive used in various industrial applications. Bonded quality is crucial for ensuring structural integrity and safety. In this study, the nonlinear non-collinear wave mixing techniques were developed to nondestructive evaluate micro imperfections in adhesive joints, including adhesive degradation and bond weakening. Two testing schemes were proposed via the resonance conditions of the adhesive and substrate respectively, following the classical nonlinearity theories. Numerical simulations and experiments of non-collinear wave mixing were conducted to explore the feasibility of the proposed testing schemes for accessing two typical micro imperfections in adhesive joints. Both the simulation and experimental results demonstrate that the proposed nonlinear non-collinear wave mixing method is effective for nondestructive evaluation of the micro imperfections in adhesive joints. Moreover, the scheme via resonance conditions of adhesive exhibits a higher sensitive to the adhesive degradation, whereas the one relying on the resonance conditions of substrate exhibits a higher sensitive to bond weakening.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778298","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

Electromagnetic Radiation Characteristics and Mechanical Properties of Cement-Mortar Under Impact Load 冲击载荷下水泥砂浆的电磁辐射特性和力学性能

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-11-11 DOI: 10.1007/s10921-024-01140-2

Amit Kumar, Vishal S. Chauhan, Rajeev Kumar, Kamal Prasad

{"title":"Electromagnetic Radiation Characteristics and Mechanical Properties of Cement-Mortar Under Impact Load","authors":"Amit Kumar, Vishal S. Chauhan, Rajeev Kumar, Kamal Prasad","doi":"10.1007/s10921-024-01140-2","DOIUrl":"10.1007/s10921-024-01140-2","url":null,"abstract":"<div><p>This study investigates the changes in electromagnetic radiation (EMR) emissions from cement-mortar subjected to impact throughout its curing process. The generation of EMR signals in hydrated samples is primarily driven by the accelerated motion of charged particles through the pore spaces and the time-dependent variation in dipole moments formed at the electrical double layer. As the hydration (curing) progresses, there is a noticeable decrease in EMR voltage, average EMR energy release rate, and dominant frequency. However, these EMR parameters exhibit an increasing trend with the application of higher mechanical impact energy. It was further observed that as hydration advances, the non-evaporable water content and degree of hydration increase, whereas the evaporable water content decreases. Additionally, EMR voltage recorded after fracture was consistently lower than that measured before fracture across all curing days, indicating that crack formation during repetitive loading suppresses EMR emissions. This suggests that cracks formed in the cement-mortar do not facilitate EMR generation. Moreover, the study found an inverse relationship between impact-dependent mechanical parameters and EMR voltage, highlighting that as mechanical resistance to impact increases, EMR voltage decreases. These findings suggest that the EMR technique has significant potential for non-contact, early-age monitoring of civil structures, providing critical insights into their mechanical integrity and performance under load.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598923","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

Instance Segmentation XXL-CT Challenge of a Historic Airplane 历史飞机的实例分割 XXL-CT 挑战赛

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-11-05 DOI: 10.1007/s10921-024-01136-y

Roland Gruber, Johann Christopher Engster, Markus Michen, Nele Blum, Maik Stille, Stefan Gerth, Thomas Wittenberg

引用次数: 0

Publisher Correction: Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery 出版商更正：基于近距离图像点云和红外图像的液化天然气外罐表面裂缝智能提取技术

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-11-03 DOI: 10.1007/s10921-024-01128-y

Ming Guo, Li Zhu, Youshan Zhao, Xingyu Tang, Kecai Guo, Yanru Shi, Liping Han

引用次数: 0

Acoustic Emission Signal Feature Extraction for Bearing Faults Using ACF and GMOMEDA 利用 ACF 和 GMOMEDA 提取轴承故障的声发射信号特征

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-11-02 DOI: 10.1007/s10921-024-01134-0

Yun Li, Yang Yu, Ping Yang, Fanzi Pu, Yunpeng Ben

{"title":"Acoustic Emission Signal Feature Extraction for Bearing Faults Using ACF and GMOMEDA","authors":"Yun Li, Yang Yu, Ping Yang, Fanzi Pu, Yunpeng Ben","doi":"10.1007/s10921-024-01134-0","DOIUrl":"10.1007/s10921-024-01134-0","url":null,"abstract":"<div><p>In industry, rolling bearing damage acoustic emission (AE) signals are interfered with by complex transmission paths and strong noise. The signal-to-noise ratio of the AE signal is low. The bearing periodic fault pulse is weak, and fault feature extraction is challenging. To address these issues, combined with the characteristics of impulsiveness and rapid attention of the AE signal, an enhancement of the bearing weak fault signal based on the autocorrelation function (ACF) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) method is proposed in this contribution. Firstly, in low signal-to-noise ratio, the target vector of the MOMEDA method is not optimal, and the diagnostic accuracy is low. To address this problem, this paper improves MOMEDA by using the gradient descent method, called GMOMEDA. Rolling bearing fault AE pulse signals are enhanced. Then, a method combination of ACF and GMOMEDA highlights the periodic elastic wave in the signal. Finally, the enhanced AE signal is processed by envelope demodulation to extract the frequency of the bearing fault signal. The experimental results show that the performance of the ACF-GMOMEDA method is better than the other five methods. The frequency features of bearing fault AE signal can be accurately extracted.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565801","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

Modeling and Analysis of Ellipticity Dispersion Characteristics of Lamb Waves in Pre-stressed Plates 预应力板中λ波的椭圆度频散特性建模与分析

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-10-25 DOI: 10.1007/s10921-024-01133-1

Yizheng Zhang, Yan Lyu, Jie Gao, Yang Zheng, Yongkang Wang, Bin Wu, Cunfu He

{"title":"Modeling and Analysis of Ellipticity Dispersion Characteristics of Lamb Waves in Pre-stressed Plates","authors":"Yizheng Zhang, Yan Lyu, Jie Gao, Yang Zheng, Yongkang Wang, Bin Wu, Cunfu He","doi":"10.1007/s10921-024-01133-1","DOIUrl":"10.1007/s10921-024-01133-1","url":null,"abstract":"<div><p>In this research, based on the weak nonlinear elasticity theory and finite deformation theory, a dynamic equation of guided waves in pre-stressed plates is established. Legendre orthogonal polynomials expansion method is employed to analytically solve wave equations. The dispersion curves and particle trajectory are obtained without root-finding algorithm. The solution is validated by comparing with the superposition of partial bulk wave method, and its convergence properties are analyzed. The effects of the pre-stress states on particle trajectory and ellipticity dispersion curves are investigated. Results confirm that the behavior of particle trajectory and ellipticity dispersion depends not only on the pre-stress states but also on frequency and mode. Next, ellipticity dispersion curve of fundamental Lamb modes with various stress state and propagation directions are calculated. Finally, the sensitivity of ellipticity as an indicator of stress is also analyzed. These results provide useful reference for the development of innovative nondestructive testing method for pre-stress states based on Lamb waves.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518588","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

Self-Calibrating Stress Measurement System Based on Multidirectional Barkhausen Noise Measurements 基于多向巴尔豪森噪声测量的自校准应力测量系统

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-10-22 DOI: 10.1007/s10921-024-01137-x

Leszek Piotrowski, Marek Chmielewski

{"title":"Self-Calibrating Stress Measurement System Based on Multidirectional Barkhausen Noise Measurements","authors":"Leszek Piotrowski, Marek Chmielewski","doi":"10.1007/s10921-024-01137-x","DOIUrl":"10.1007/s10921-024-01137-x","url":null,"abstract":"<div><p>The system presented in this paper enables automatization of the two-dimensional calibration process (determination of Barkhausen noise (BN) intensity dependence on in-plane components of strain). Then, using dedicated software created by the authors in LabVIEW environment, and with the help of two dimensional calibration data one can effectively determine strain and stress distribution i.e. magnitude and orientation of main strain/stress components relative to measurement direction. BN signal measurements are performed using an advanced, multidirectional Barkhausen noise (BN) measuring sensor and a measurement system dedicated for cooperation with it. The system uses a robust algorithm for the strain components determination based on calibration surfaces, instead of usually applied curves, thus taking the influence of normal strain component directly into account instead of treating it as a correction factor (if not completely neglecting). The originality of the system arises also from the fact that this is the first BN measurement system that is self-calibrating (i.e. automatically loads the calibration sample in a pre-programmed way, performs BN signal measurements and calculates calibration planes), provided that the user possesses enough of the investigated material for calibration sample preparation.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01137-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518452","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

Investigation of Stress Concentration and Microdefect Identification in Ferromagnetic Materials within a Geomagnetic Field 地磁场中铁磁材料的应力集中和微缺陷识别研究

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-10-15 DOI: 10.1007/s10921-024-01135-z

Bo Hu, Weilong Chong, Wenze Shi, Fasheng Qiu

{"title":"Investigation of Stress Concentration and Microdefect Identification in Ferromagnetic Materials within a Geomagnetic Field","authors":"Bo Hu, Weilong Chong, Wenze Shi, Fasheng Qiu","doi":"10.1007/s10921-024-01135-z","DOIUrl":"10.1007/s10921-024-01135-z","url":null,"abstract":"<div><p>Local damage or stress concentration that forms during manufacturing and long-term use of ferromagnetic materials has a direct impact on the safety of engineering structures. Thus, accurately identifying damage and stress conditions in these materials is crucial. In this study, martensitic stainless steel, a type of ferromagnetic material, is chosen as the subject for investigation. A weak magnetic detection device is engineered specifically for this purpose, and tests are conducted on the material using this device. The stress value of the material is determined using X-ray diffraction, while magnetic induction intensity is simultaneously recorded with a weak magnetic detection device along the same path. The stress value and magnetic induction intensity are normalized, and the results are analyzed to establish a correlation between weak magnetic signals and stress. Then, a signal processing technique combining blind source separation and eigenvalue recognition is introduced to achieve stress concentration and microdefect location identification. This method is based on the correlation analysis results between weak magnetic signals and stress, as well as supporting evidence from prior studies. The experimental results demonstrate that the location of stress concentration can be accurately determined by identifying the peak or valley value of weak magnetic signals, with an error range of less than 30%. The algorithm of blind source separation and eigenvalue recognition can pinpoint the location of stress concentration and microdefects from the obtained signal. This study presents a novel nondestructive testing method for stress concentration and microdefect identification in ferromagnetic materials.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438782","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