Journal of Nondestructive Evaluation最新文献_第10页

Characterizing Changes in a Salt Hydrate Bed Using Micro X-Ray Computed Tomography 利用显微 X 射线计算机断层扫描表征盐水合物床的变化

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01092-7

Aastha Arya, Jorge Martinez-Garcia, Philipp Schuetz, Amirhoushang Mahmoudi, Gerrit Brem, Pim A. J. Donkers, Mina Shahi

{"title":"Characterizing Changes in a Salt Hydrate Bed Using Micro X-Ray Computed Tomography","authors":"Aastha Arya, Jorge Martinez-Garcia, Philipp Schuetz, Amirhoushang Mahmoudi, Gerrit Brem, Pim A. J. Donkers, Mina Shahi","doi":"10.1007/s10921-024-01092-7","DOIUrl":"10.1007/s10921-024-01092-7","url":null,"abstract":"<div><p>Thermochemical storage using salt hydrates presents a promising energy storage method. Ensuring the long-term effectiveness of the system is critical, demanding both chemical and mechanical stability of material for repetitive cycling. Challenges arise from agglomeration and volume variations during discharging and charging, impacting the cyclability of thermochemical materials (TCM). For practical usage, the material is often used in a packed bed containing millimetre-sized grains. A micro-level analysis of changes in a packed bed system, along with a deeper understanding involving quantifying bed characteristics, is crucial. In this study, micro X-ray computed tomography (XCT) is used to compare changes in the packed bed before and after cycling the material. Findings indicate a significant decrease in pore size distribution in the bed after 10 cycles and a decrease in porosity from 41.34 to 19.91% accompanied by an increase in grain size, reducing void space. A comparison of effective thermal conductivity between the uncycled and cycled reactor indicates an increase after cycling. Additionally, the effective thermal conductivity is lower in the axial direction compared to the radial. XCT data from uncycled and cycled experiments are further used to observe percolation paths inside the bed. Furthermore, at a system scale fluid flow profile comparison is presented for uncycled and cycled packed beds. It has been observed that the permeability decreased and the pressure drop increased from 0.31 to 4.88 Pa after cycling.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01092-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372260","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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks 减少神经网络解读激光超声信号的训练数据

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01090-9

Janez Rus, Romain Fleury

{"title":"Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks","authors":"Janez Rus, Romain Fleury","doi":"10.1007/s10921-024-01090-9","DOIUrl":"10.1007/s10921-024-01090-9","url":null,"abstract":"<div><p>The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens with known, unchangeable defect properties, which are usually complicated to fabricate. It consist of a shape memory polymer foil with temperature-dependent Young’s modulus and ultrasound attenuation. This open a possibility to generate a reconfigurable defect by projecting a heating laser in the form of a short line on the specimen surface. Ultrasound is generated by a laser pulse at one fixed position and detected by a laser vibrometer at another fixed position for 64 different defect positions and 3 different configurations of the specimen. The obtained diversified datasets are used to optimize the neural network architecture for the interpretation of ultrasound signals. We study the performance of the model in cases of reduced and dissimilar training datasets. In our first study, we classify the specimen configurations with the defect position being the disturbing parameter. The model shows high performance on a dataset of signals obtained at all the defect positions, even if trained on a completely different dataset containing signals obtained at only few defect positions. In our second study, we perform precise defect localization. The model becomes robust to the changes in the specimen configuration when a reduced dataset, containing signals obtained at two different specimen configurations, is used for the training process. This work highlights the potential of the demonstrated machine learning algorithm for industrial quality control. High-volume products (simulated by a reconfigurable specimen in our work) can be rapidly tested on the production line using this single-point and contact-free laser ultrasonic method.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01090-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372528","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

Phased Array Ultrasonic Testing on Thick Glass Fiber Reinforced Thermoplastic Composite Pipe Implementing the Classical Time-Corrected Gain Method 采用经典时间校正增益法对厚玻璃纤维增强热塑性复合材料管道进行相控阵超声波测试

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01096-3

Mohd Fadzil Mohd Tahir, Andreas T. Echtermeyer

{"title":"Phased Array Ultrasonic Testing on Thick Glass Fiber Reinforced Thermoplastic Composite Pipe Implementing the Classical Time-Corrected Gain Method","authors":"Mohd Fadzil Mohd Tahir, Andreas T. Echtermeyer","doi":"10.1007/s10921-024-01096-3","DOIUrl":"10.1007/s10921-024-01096-3","url":null,"abstract":"<div><p>Thermoplastic composite pipe is gaining popularity in the oil and gas and renewable energy industries as an alternative to traditional metal pipe mainly due to its capability of being spooled onto a reel and exceptional corrosion resistance properties. Despite its corrosion-proof nature, this material remains susceptible to various defects, such as delamination, fiber breakage, matrix degradation and deformation. This study employed the phased array ultrasonic testing technique with the implementation of the classical time-corrected gain method to compensate for the significant spatial signal attenuation beyond the first interface layer in the thick multi-layered thermoplastic composite pipe. Initially, the ultrasonic signals from the interface layers and back wall were detected with good signal-to-noise ratios. Subsequently, flat-bottom holes of varying depths, simulating one-sided delamination, were bored and the proposed method effectively identified ultrasonic signals from these holes, clearly distinguishing them from the background noise and interface layer signals. Finally, a defect deliberately fabricated within the composite laminate layers during the pipe manufacturing process was successfully identified. Subsequently, this fabricated defect was visualized in a three-dimensional representation using the X-ray computed tomography for a qualitative and quantitative comparison with the proposed ultrasonic method, showing a high level of agreement.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01096-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374780","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

X-Ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study 作为实时检测性能预测方法的 X 射线图像生成：案例研究

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01091-8

Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, K. Joost Batenburg

{"title":"X-Ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study","authors":"Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, K. Joost Batenburg","doi":"10.1007/s10921-024-01091-8","DOIUrl":"10.1007/s10921-024-01091-8","url":null,"abstract":"<div><p>X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using Probability of Detection curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519646","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

A Polynomial Approach for Thermoelastic Wave Propagation in Functionally Gradient Material Plates 功能梯度材料板中热弹性波传播的多项式方法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01087-4

Xiaolei Lin, Yan Lyu, Jie Gao, Cunfu He

{"title":"A Polynomial Approach for Thermoelastic Wave Propagation in Functionally Gradient Material Plates","authors":"Xiaolei Lin, Yan Lyu, Jie Gao, Cunfu He","doi":"10.1007/s10921-024-01087-4","DOIUrl":"10.1007/s10921-024-01087-4","url":null,"abstract":"<div><p>Functionally gradient material (FGM) in service often experience temperature variations that can affect the propagation characteristics of guided waves. This investigation aims to study the propagation of thermoelastic guided waves in the FGM plate. A computational method for the state vector and Legendre polynomials hybrid approach, which is proposed based on the Green–Nagdhi theory of thermoelasticity. The heat conduction equation is introduced into the governing equations, and optimized using univariate nonlinear regression for arbitrary gradient distributions of the material components. To study their dispersion characteristics, a non-hierarchical calculation for the dispersion curves of FGM plates versus temperature is realized. In addition, a frequency domain simulation model is developed and compared with theoretical data to evaluate the accuracy and feasibility of the proposed theory. Then, the influence of Legendre orthogonal polynomial cut-off order on dispersion curve convergence is investigated. Subsequently, the shift of the gradient index and temperature variation on the fundamental mode in dispersion curve is analyzed. The results indicate that changes in both gradient index and temperature lead to a systematic shift in the phase velocity of fundamental modes in the low frequency range. Meanwhile, anti-symmetric modes exhibit higher sensitivity. On this basis, the study can provide theoretical support for the acoustic non-destructive characterization of FGM plates versus temperature.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372252","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

An Analytical Model to Evaluate the Volumetric Strain in a Polymeric Material Using Terahertz Time-Domain Spectroscopy 利用太赫兹时域光谱评估聚合物材料体积应变的分析模型

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01095-4

Sushrut Karmarkar, Mahavir Singh, Vikas Tomar

{"title":"An Analytical Model to Evaluate the Volumetric Strain in a Polymeric Material Using Terahertz Time-Domain Spectroscopy","authors":"Sushrut Karmarkar, Mahavir Singh, Vikas Tomar","doi":"10.1007/s10921-024-01095-4","DOIUrl":"10.1007/s10921-024-01095-4","url":null,"abstract":"<div><p>This work develops a polarization-dependent analytical model using terahertz time-domain spectroscopy (THz-TDS) for computing strain in materials. The model establishes a correlation between volumetric strain and the change in time of arrival for a THz pulse by using the dielectrostrictive properties, variations in doping particle density, and changes in the thickness of the sample resulting from Poisson’s effects. The analytical model is validated through strain mapping of polydimethylsiloxane (PDMS) doped with passive highly dielectrostrictive strontium titanate (STO). Two experiments, using an open-hole tensile and a circular edge-notch specimen are conducted to show the efficacy of the proposed. The stress relaxation behavior of the composite is measured and accounted for to prevent changes in strain during the measurement window. The THz strain mapping results are compared with the finite element model (FEM) and surface strain measurements using the digital image correlation (DIC) method. The experimental findings exhibit sensitivity to material features such as particle clumping and edge effects. The THz strain map shows a strong agreement with FEM and DIC results, thus demonstrating the applicability of this technique for surface and sub-surface strain mapping in polymeric composites.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372731","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

A Highly Efficient and Lightweight Detection Method for Steel Surface Defect 一种高效轻便的钢铁表面缺陷检测方法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01084-7

Changyu Xu, Jie Li, Xianguo Li

引用次数: 0

Automatic Defect Classification for Infrared Thermography in CFRP based on Deep Learning Dense Convolutional Neural Network 基于深度学习密集卷积神经网络的 CFRP 红外热成像缺陷自动分类技术

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01089-2

Guozeng Liu, Weicheng Gao, Wei Liu, Yijiao Chen, Tianlong Wang, Yongzhi Xie, Weiliang Bai, Zijing Li

{"title":"Automatic Defect Classification for Infrared Thermography in CFRP based on Deep Learning Dense Convolutional Neural Network","authors":"Guozeng Liu, Weicheng Gao, Wei Liu, Yijiao Chen, Tianlong Wang, Yongzhi Xie, Weiliang Bai, Zijing Li","doi":"10.1007/s10921-024-01089-2","DOIUrl":"10.1007/s10921-024-01089-2","url":null,"abstract":"<div><p>Carbon fiber reinforced polymer (CFRP) is an important composite material widely used in aerospace and other industries. However, long-term service in harsh environments can lead to various defects such as debonding, delamination, water, cracks, etc. Therefore, it becomes imperative to conduct non-destructive testing (NDT) on CFRP to ensure its structural integrity and safety. Infrared thermography was employed for defect classification in CFRP laminate and CFRP honeycomb sandwich composites (HSC) by applied a convolutional neural networks (CNN). The proposed automatic defect classification method based on CNN is one of the goals of NDE 4.0 to apply advanced technologies (such as deep learning and AI) to improve NDT efficiency and accuracy. The infrared detection dataset consisted of five classes: debonding, water, delamination, crack, and health. To effectively expand the dataset, offline data augmentation technique were employed. A deep learning technique of Dense convolutional neural network (DCNN) were proposed to defect classification. AlexNet, VGG-16, ResNet-50 and DenseNet-121 based on transfer learning fine-tuning model was applied to classify debonding, water, delamination, crack and health. The classification results were analyzed by using a confusion matrix. The results shown that the accuracy of AlexNet, VGG-16, ResNet-50 and DenseNet-121 were 92.34%, 82.86%, 88.30%, 98.48%, respectively. DenseNet-121 demonstrates good performance in defect detection and recognition with an accuracy of 98.48%, and DenseNet-121 has high application potential in accurately classify and recognize defects in deep learning technique.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371043","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

A Vision-Based Displacement Measurement Method of Wind Turbine Blades in Biaxial Fatigue Testing 双轴疲劳测试中基于视觉的风力涡轮机叶片位移测量方法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-06-07 DOI: 10.1007/s10921-024-01097-2

Xinyuan Yang, Qiang Ma, Xuezong Bai, Huidong Ma, Zongwen An

{"title":"A Vision-Based Displacement Measurement Method of Wind Turbine Blades in Biaxial Fatigue Testing","authors":"Xinyuan Yang, Qiang Ma, Xuezong Bai, Huidong Ma, Zongwen An","doi":"10.1007/s10921-024-01097-2","DOIUrl":"10.1007/s10921-024-01097-2","url":null,"abstract":"<div><p>This paper introduces a vision-based displacement measurement method for wind turbine blades in biaxial fatigue testing. Instead of relying on existing strain data, this method collects displacement data to control the loading system. The main idea of this method is to update the pixel radius of the target point. The ratio of the pixel radius of the target point to the actual radius is used as a reference to update the displacement conversion coefficient <i>λ</i> of the next frame image in real-time. Through both static and dynamic experiments, the accuracy and superiority of this method have been verified, and the feasibility of using displacement instead of strain to control fatigue loading has been validated. The data demonstrates that the measurement error between the proposed method and the electronic total station remains within 10%. Compared to the results obtained by the traditional methods, the proposed method has shown significant improvement. The vision-based displacement measurement method not only ensures accuracy but also reduces the complexity of testing, providing more possibilities for fatigue testing of wind turbine blades.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374657","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

Micromagnetic and Quantitative Prediction of Yield and Tensile Strength of Carbon Steels Using Transfer Learning Method 利用迁移学习法对碳钢的屈服强度和拉伸强度进行微观和定量预测

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2024-05-21 DOI: 10.1007/s10921-024-01086-5

Xianxian Wang, Cunfu He, Peng Li, Xiucheng Liu, Zhixiang Xing, Mengshuai Ning

{"title":"Micromagnetic and Quantitative Prediction of Yield and Tensile Strength of Carbon Steels Using Transfer Learning Method","authors":"Xianxian Wang, Cunfu He, Peng Li, Xiucheng Liu, Zhixiang Xing, Mengshuai Ning","doi":"10.1007/s10921-024-01086-5","DOIUrl":"10.1007/s10921-024-01086-5","url":null,"abstract":"<div><p>This study investigates the correlation between various micromagnetic signature patterns and the yield and tensile strengths of carbon steel (Cr12MoV steel as per Chinese standards). For this purpose, back-propagation neural network (BP-NN) models are established to quantitatively predict the yield and tensile strengths of carbon steels. The accuracy of prediction models is significantly affected by the presence of redundant micromagnetic signature patterns. By carefully screening the input parameters, it is able to effectively mitigate prediction errors arising from unreasonable model inputs. In the field of micromagnetic nondestructive testing (NDT), prediction models calibrated for a specific instrument or sensor cannot be directly applied to another instrument or sensor. In the study, a joint distribution adaptation transfer learning strategy based on auxiliary data is proposed to enhance the generalization of prediction models for cross-instrument applications. When auxiliary data accounts for 30% of the source domain data, the joint distribution adaptation transfer learning method based on auxiliary data improves the robustness of the model. The accuracy of the yield strength and tensile strength calibration models witnesses remarkable improvements of approximately 91.4% and 93.5%, respectively.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141114165","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