Pub Date : 2024-10-18DOI: 10.1016/j.ndteint.2024.103260
Han Yu, Xingjie Li, Huasheng Xie, Xinyue Li, Chunyu Hou
Deep learning methodologies have gained substantial traction for defect recognition in industrial radiographic testing including welds, castings and other fields. Regardless of the deep learning utilized, it has emerged as a standard configuration to use a model pre-trained from ImageNet to accelerate convergence and enhance recognition accuracy. However, there is a significant gap between the domain of natural images and industrial radiographs, raising the question of whether there might be a superior pre-training method than relying on ImageNet pre-training. Fortunately, medical radiographs are more similar to industrial radiographs than natural images because of the same imaging method. In this paper, we initially utilize numerous medical radiographic images from CheXpert dataset to train a pre-trained CNN model. Then, we apply this model to four distinct tasks within two radiographic testing scenarios to validate its advantages and generalization capabilities. Finally, experiments on multiple datasets indicate that our method brings more benefits than ImageNet pre-training or training from scratch, with a F1 score improvement of 3.41 %–13.72 % for defect classification and a mIoU improvement of 1.05 %–6.58 % for defect segmentation. It demonstrates that pre-training from medical radiographs provides a cost-free improvement for all kinds of tasks in industrial defect recognition.
{"title":"Improving the industrial defect recognition in radiographic testing by pre-training on medical radiographs","authors":"Han Yu, Xingjie Li, Huasheng Xie, Xinyue Li, Chunyu Hou","doi":"10.1016/j.ndteint.2024.103260","DOIUrl":"10.1016/j.ndteint.2024.103260","url":null,"abstract":"<div><div>Deep learning methodologies have gained substantial traction for defect recognition in industrial radiographic testing including welds, castings and other fields. Regardless of the deep learning utilized, it has emerged as a standard configuration to use a model pre-trained from ImageNet to accelerate convergence and enhance recognition accuracy. However, there is a significant gap between the domain of natural images and industrial radiographs, raising the question of whether there might be a superior pre-training method than relying on ImageNet pre-training. Fortunately, medical radiographs are more similar to industrial radiographs than natural images because of the same imaging method. In this paper, we initially utilize numerous medical radiographic images from CheXpert dataset to train a pre-trained CNN model. Then, we apply this model to four distinct tasks within two radiographic testing scenarios to validate its advantages and generalization capabilities. Finally, experiments on multiple datasets indicate that our method brings more benefits than ImageNet pre-training or training from scratch, with a F1 score improvement of 3.41 %–13.72 % for defect classification and a mIoU improvement of 1.05 %–6.58 % for defect segmentation. It demonstrates that pre-training from medical radiographs provides a cost-free improvement for all kinds of tasks in industrial defect recognition.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"149 ","pages":"Article 103260"},"PeriodicalIF":4.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.ndteint.2024.103251
Weixin Wang, Jie Zhang, Paul D. Wilcox
Ultrasound backscattering signals from material microstructures can be used to evaluate the material microstructure grain size. This typically involves making pulse-echo immersion measurements at multiple locations using a focused ultrasonic transducer in order to obtain an accurate estimate of the root-mean-square amplitude of the back-scattered signal at a specified focal position. However, this restricts some practical applications of using such techniques in, for example, on-line measurements in high-value manufacturing and in-service inspections where multiple immersion measurements are not feasible to use. The main benefit of using ultrasonic phased arrays is that one array probe at one position can focus ultrasound beams at multiple points using different focal laws either physically or in data postprocessing. Potentially this means that accurate grain size measurements can be obtained from a single array measurement. In this paper, the classic backscattering method for conventional transducers is adapted to be used for full matrix capture datasets from an ultrasonic array. Three-dimensional ultrasonic models are developed in the proposed inverse process to measure material microstructure grain size. Experimental validations were performed on two metallic materials: copper (EN1652) and bright mild steel (BS970). A good agreement is shown between the experimentally measured grain sizes from array data and metallography measurements. Compared to the classic pulse-echo immersion back-scattering measurements, the proposed method enables accurate measurement of grain size in a direct contact configuration at fewer locations. This has potential to make on-line grain size measurements possible.
{"title":"Metallic material microstructure grain size measurements from backscattering signals in ultrasonic array data sets","authors":"Weixin Wang, Jie Zhang, Paul D. Wilcox","doi":"10.1016/j.ndteint.2024.103251","DOIUrl":"10.1016/j.ndteint.2024.103251","url":null,"abstract":"<div><div>Ultrasound backscattering signals from material microstructures can be used to evaluate the material microstructure grain size. This typically involves making pulse-echo immersion measurements at multiple locations using a focused ultrasonic transducer in order to obtain an accurate estimate of the root-mean-square amplitude of the back-scattered signal at a specified focal position. However, this restricts some practical applications of using such techniques in, for example, on-line measurements in high-value manufacturing and in-service inspections where multiple immersion measurements are not feasible to use. The main benefit of using ultrasonic phased arrays is that one array probe at one position can focus ultrasound beams at multiple points using different focal laws either physically or in data postprocessing. Potentially this means that accurate grain size measurements can be obtained from a single array measurement. In this paper, the classic backscattering method for conventional transducers is adapted to be used for full matrix capture datasets from an ultrasonic array. Three-dimensional ultrasonic models are developed in the proposed inverse process to measure material microstructure grain size. Experimental validations were performed on two metallic materials: copper (EN1652) and bright mild steel (BS970). A good agreement is shown between the experimentally measured grain sizes from array data and metallography measurements. Compared to the classic pulse-echo immersion back-scattering measurements, the proposed method enables accurate measurement of grain size in a direct contact configuration at fewer locations. This has potential to make on-line grain size measurements possible.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"149 ","pages":"Article 103251"},"PeriodicalIF":4.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The mechanical properties of steel are determined by its microstructure, which is closely related to its permeability profile. In thermal processing, layered structures are formed in steel and different layers have different mechanical and magnetic properties. Therefore, it is crucial to propose a practical method to monitor the change of permeability profile along the depth, which can indicate the evolution of the microstructure of steel during thermal processing, such as hot rolling. This paper presents a method for determining the layered structure and permeability profile of the steel by using pulsed eddy current testing (PECT), which offers better penetration ability. An analytical model has been deduced for calculating the time-domain pulsed eddy current (PEC) response from a Hall sensor of a triple-layer conductor system based on the inverse Laplace transform. It is found the Tau () curve is closely related to the permeability profile of the conductor. For the inverse solution, the Simultaneous Iterative Reconstruction Technique (SIRT) is utilized to determine the permeability profile of the multilayered specimens from the measured response. The approximate Jacobian matrix (sensitivity matrix) is obtained by the perturbation method based on the Tau curve. However, the permeability profile suffers from smoothing effect and sharp features are lost. Deep learning (DL) algorithm based on the Multi-Scale 1D-ResNet model is therefore introduced to address this issue. Numerical simulations and experiments have been performed to evaluate the proposed method for permeability profile estimation with various materials and thicknesses. The DL method can achieve an accurate estimation of the plate permeability profile with a relative error under 5%.
钢的机械性能由其微观结构决定，而微观结构与钢的磁导率曲线密切相关。在热加工过程中，钢中会形成层状结构，不同的层具有不同的机械和磁性能。因此，提出一种实用的方法来监测沿深度方向的渗透率曲线变化至关重要，这种方法可以显示钢在热加工（如热轧）过程中微观结构的演变。本文提出了一种利用脉冲涡流测试（PECT）确定钢材分层结构和渗透率分布的方法，这种方法具有更好的穿透能力。基于反拉普拉斯变换，本文推导出一个分析模型，用于计算三层导体系统霍尔传感器的时域脉冲涡流（PEC）响应。结果发现 Tau (τ) 曲线与导体的磁导率曲线密切相关。在反求解时，利用同步迭代重构技术（SIRT）从测量响应确定多层试样的渗透率剖面。通过基于 Tau 曲线的扰动法获得近似雅各矩阵（灵敏度矩阵）。然而，渗透率剖面受到平滑效应的影响，失去了鲜明的特征。因此，我们引入了基于多尺度 1D-ResNet 模型的深度学习（DL）算法来解决这一问题。我们进行了数值模拟和实验，以评估针对不同材料和厚度的渗透率剖面估算所提出的方法。DL 方法可以实现板渗透率剖面的精确估算，相对误差低于 5%。
{"title":"Analyzing the permeability distribution of multilayered specimens using pulsed eddy-current testing with multi-scale 1D-ResNet","authors":"Xinnan Zheng, Saibo She, Zihan Xia, Lei Xiong, Xun Zou, Kuohai Yu, Rui Guo, Ruoxuan Zhu, Zili Zhang, Wuliang Yin","doi":"10.1016/j.ndteint.2024.103247","DOIUrl":"10.1016/j.ndteint.2024.103247","url":null,"abstract":"<div><div>The mechanical properties of steel are determined by its microstructure, which is closely related to its permeability profile. In thermal processing, layered structures are formed in steel and different layers have different mechanical and magnetic properties. Therefore, it is crucial to propose a practical method to monitor the change of permeability profile along the depth, which can indicate the evolution of the microstructure of steel during thermal processing, such as hot rolling. This paper presents a method for determining the layered structure and permeability profile of the steel by using pulsed eddy current testing (PECT), which offers better penetration ability. An analytical model has been deduced for calculating the time-domain pulsed eddy current (PEC) response from a Hall sensor of a triple-layer conductor system based on the inverse Laplace transform. It is found the Tau (<span><math><mi>τ</mi></math></span>) curve is closely related to the permeability profile of the conductor. For the inverse solution, the Simultaneous Iterative Reconstruction Technique (SIRT) is utilized to determine the permeability profile of the multilayered specimens from the measured response. The approximate Jacobian matrix (sensitivity matrix) is obtained by the perturbation method based on the Tau curve. However, the permeability profile suffers from smoothing effect and sharp features are lost. Deep learning (DL) algorithm based on the Multi-Scale 1D-ResNet model is therefore introduced to address this issue. Numerical simulations and experiments have been performed to evaluate the proposed method for permeability profile estimation with various materials and thicknesses. The DL method can achieve an accurate estimation of the plate permeability profile with a relative error under 5%.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"149 ","pages":"Article 103247"},"PeriodicalIF":4.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-13DOI: 10.1016/j.ndteint.2024.103250
Rongbiao Wang , Yongzhi Chen , Haozhi Yu , Zhiyuan Xu , Jian Tang , Bo Feng , Yihua Kang , Kai Song
Pipelines play a crucial role in industries such as petroleum and nuclear power, where non-destructive testing is essential. Magnetic flux leakage testing methods are widely used for pipeline inspection due to their ability to detect both internal and external defects. However, accurately classifying and evaluating the size of such defects poses challenges due to the complex coupling relationship between ferromagnetic materials and defect magnetic fields. This paper proposes a method for defect classification and quantification based on the time domain characteristics of AC magnetic flux leakage signals. Firstly, the paper explores the shielding effects caused by transient high magnetic permeability within the material on signals from internal defects. It analyzes the differences in signals between internal and external defects. Then, based on the nonlinear attributes of defect signals, the paper proposes a defect classification method based on derivatives analysis of the windowed time-domain signal. Moreover, the study finds that rising time and zero-crossing time can be used to assess the depth of internal and external defects separately, which can decouple the width and depth. Finally, experimental validation confirms the effectiveness of defects classification and quantification. This paper provides a feasible method for evaluating the defect of ferromagnetic materials.
{"title":"Defect classification and quantification method based on AC magnetic flux leakage time domain signal characteristics","authors":"Rongbiao Wang , Yongzhi Chen , Haozhi Yu , Zhiyuan Xu , Jian Tang , Bo Feng , Yihua Kang , Kai Song","doi":"10.1016/j.ndteint.2024.103250","DOIUrl":"10.1016/j.ndteint.2024.103250","url":null,"abstract":"<div><div>Pipelines play a crucial role in industries such as petroleum and nuclear power, where non-destructive testing is essential. Magnetic flux leakage testing methods are widely used for pipeline inspection due to their ability to detect both internal and external defects. However, accurately classifying and evaluating the size of such defects poses challenges due to the complex coupling relationship between ferromagnetic materials and defect magnetic fields. This paper proposes a method for defect classification and quantification based on the time domain characteristics of AC magnetic flux leakage signals. Firstly, the paper explores the shielding effects caused by transient high magnetic permeability within the material on signals from internal defects. It analyzes the differences in signals between internal and external defects. Then, based on the nonlinear attributes of defect signals, the paper proposes a defect classification method based on derivatives analysis of the windowed time-domain signal. Moreover, the study finds that rising time and zero-crossing time can be used to assess the depth of internal and external defects separately, which can decouple the width and depth. Finally, experimental validation confirms the effectiveness of defects classification and quantification. This paper provides a feasible method for evaluating the defect of ferromagnetic materials.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"149 ","pages":"Article 103250"},"PeriodicalIF":4.1,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-12DOI: 10.1016/j.ndteint.2024.103249
Le Quang Trung , Naoya Kasai , Minhhuy Le , Kouichi Sekino
This study presents an advanced FEC sensor, engineered by arranging coils in a co-directional current configuration. Moreover, boasting a compact design, the FEC sensor showcases significantly enhanced spatial resolution, enabling robust detection of small cracks even at low excitation frequencies and mitigating issues of overlapping in adjacent crack detection. Results indicate successful crack detection through voltage and phase measurements, albeit with phase signals demonstrating variation at specific excitation frequencies, complicating the determination of actual crack sizes. Consequently, a novel model is proposed to forecast actual crack sizes, leveraging experimental data from the FEC sensor system. This model integrates a Residual Neural Network (ResNet) architecture with a Convolutional Block Attention Module (CBAM) and utilizes the Huber loss function to minimize errors during model training. Comparative analysis underscores the superior performance of the proposed model in predicting crack length and depth compared to the standalone ResNet, particularly when utilizing the Huber loss function with a δ value of 1.0. Evaluation metrics, encompassing Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), illustrate an average accuracy surpassing 95 % for crack size predictions. Consequently, the proposed model demonstrates remarkable performance, significantly reducing the time required to ascertain actual crack sizes by leveraging voltage and phase measurements.
{"title":"Predicting actual crack size through crack signal obtained by advanced Flexible Eddy Current Sensor using ResNet integrated with CBAM and Huber loss function","authors":"Le Quang Trung , Naoya Kasai , Minhhuy Le , Kouichi Sekino","doi":"10.1016/j.ndteint.2024.103249","DOIUrl":"10.1016/j.ndteint.2024.103249","url":null,"abstract":"<div><div>This study presents an advanced FEC sensor, engineered by arranging coils in a co-directional current configuration. Moreover, boasting a compact design, the FEC sensor showcases significantly enhanced spatial resolution, enabling robust detection of small cracks even at low excitation frequencies and mitigating issues of overlapping in adjacent crack detection. Results indicate successful crack detection through voltage and phase measurements, albeit with phase signals demonstrating variation at specific excitation frequencies, complicating the determination of actual crack sizes. Consequently, a novel model is proposed to forecast actual crack sizes, leveraging experimental data from the FEC sensor system. This model integrates a Residual Neural Network (ResNet) architecture with a Convolutional Block Attention Module (CBAM) and utilizes the Huber loss function to minimize errors during model training. Comparative analysis underscores the superior performance of the proposed model in predicting crack length and depth compared to the standalone ResNet, particularly when utilizing the Huber loss function with a δ value of 1.0. Evaluation metrics, encompassing Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), illustrate an average accuracy surpassing 95 % for crack size predictions. Consequently, the proposed model demonstrates remarkable performance, significantly reducing the time required to ascertain actual crack sizes by leveraging voltage and phase measurements.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"149 ","pages":"Article 103249"},"PeriodicalIF":4.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.ndteint.2024.103244
Guangyan Cui , Yanhui Wang , Yujie Li , Feifei Hou , Jie Xu
Quantitatively detecting voids behind tunnel linings presents significant challenges in identifying the range of width and depth. This paper develops an innovative method for identifying defective regions of voids based on Ground Penetrating Radar (GPR) data. This method involves three steps: Firstly, the void-identifying-feature-set (VIFS) is constructed by extracting the Amplitude peak (AT), Signal energy (ET), and Amplitude peak of the first intrinsic mode function (IMF1) component (AH) of every A-scan signal. Secondly, the Support Vector Machine (SVM) is utilized to identify defect signals and normal signals, contributing to the width identification of void in the horizontal direction. Thirdly, an innovative Three-Stage-Boundary-Extraction (TSBE) algorithm is proposed to identify the depth range of voids in the vertical direction. Experimental results conducted on both field data and simulated data demonstrated that the Intersection over Union (IOU) value and consumption time of three groups of GPR data (Data I, Data II, and Data V) are 0.739 and 0.888 s, respectively. The average IOU and consumption time of the TSBE algorithm are 0.739 and 0.058 s, respectively.
{"title":"Intelligent identification of defective regions of voids in tunnels based on GPR data","authors":"Guangyan Cui , Yanhui Wang , Yujie Li , Feifei Hou , Jie Xu","doi":"10.1016/j.ndteint.2024.103244","DOIUrl":"10.1016/j.ndteint.2024.103244","url":null,"abstract":"<div><div>Quantitatively detecting voids behind tunnel linings presents significant challenges in identifying the range of width and depth. This paper develops an innovative method for identifying defective regions of voids based on Ground Penetrating Radar (GPR) data. This method involves three steps: Firstly, the void-identifying-feature-set (<em>VIFS</em>) is constructed by extracting the Amplitude peak (<em>A</em><sub><em>T</em></sub>), Signal energy (<em>E</em><sub><em>T</em></sub>), and Amplitude peak of the first intrinsic mode function (IMF1) component (<em>A</em><sub><em>H</em></sub>) of every A-scan signal. Secondly, the Support Vector Machine (SVM) is utilized to identify defect signals and normal signals, contributing to the width identification of void in the horizontal direction. Thirdly, an innovative Three-Stage-Boundary-Extraction (TSBE) algorithm is proposed to identify the depth range of voids in the vertical direction. Experimental results conducted on both field data and simulated data demonstrated that the Intersection over Union (IOU) value and consumption time of three groups of GPR data (Data I, Data II, and Data V) are 0.739 and 0.888 s, respectively. The average IOU and consumption time of the TSBE algorithm are 0.739 and 0.058 s, respectively.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"149 ","pages":"Article 103244"},"PeriodicalIF":4.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.ndteint.2024.103245
Yijiao Ma , Wenyi Xu , Jinrong Qi , Xue Yang , Lichun Feng , Xiaoli Li , Ning Tao , Cunlin Zhang , Jiangang Sun
In this study, a photothermal nondestructive method was proposed to measure the material parameters of semi-transparent or translucent thermal barrier coatings (TBCs). We derived a theoretical model for the photothermal signal from a two-layer semi-infinite material system with a translucent first layer after a pulse laser excitation. Its solution was verified by numerical solution. A data regression algorithm based on a least-squares fitting was used for the determination of the material parameters in the translucent first layer material. To verify this new method, an experimental system was set up with a pulse laser for thermal excitation and an infrared camera for image acquisition of the thermal emission transient from several translucent EBPVD TBC samples. The predicted coating thickness is consistent with the measured value by an optical microscope. The predicted thermal conductivity and optical attenuation coefficients in the absorption and emission band are found to be in good agreement with reference values.
{"title":"Photothermal measurement of material properties for translucent thermal barrier coatings","authors":"Yijiao Ma , Wenyi Xu , Jinrong Qi , Xue Yang , Lichun Feng , Xiaoli Li , Ning Tao , Cunlin Zhang , Jiangang Sun","doi":"10.1016/j.ndteint.2024.103245","DOIUrl":"10.1016/j.ndteint.2024.103245","url":null,"abstract":"<div><div>In this study, a photothermal nondestructive method was proposed to measure the material parameters of semi-transparent or translucent thermal barrier coatings (TBCs). We derived a theoretical model for the photothermal signal from a two-layer semi-infinite material system with a translucent first layer after a pulse laser excitation. Its solution was verified by numerical solution. A data regression algorithm based on a least-squares fitting was used for the determination of the material parameters in the translucent first layer material. To verify this new method, an experimental system was set up with a pulse laser for thermal excitation and an infrared camera for image acquisition of the thermal emission transient from several translucent EBPVD TBC samples. The predicted coating thickness is consistent with the measured value by an optical microscope. The predicted thermal conductivity and optical attenuation coefficients in the absorption and emission band are found to be in good agreement with reference values.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"148 ","pages":"Article 103245"},"PeriodicalIF":4.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-29DOI: 10.1016/j.ndteint.2024.103243
Feiyang Sun , Jing Zhang , Xingyu Chen , Liyue Xu , Gaorui Chen , Kangning Jia , Li Fan , Xiaodong Xu , Liping Cheng , Xuejun Yan , Peilong Yuan , Shuyi Zhang
The health monitoring of cylindrical elements is particularly important for the safe operation of industrial production, because cylinders bear a lot of support and transmission work. Normal non-destructive evaluation techniques need special designs to suit high curvature surfaces of cylinders. Laser ultrasonic (LU) method can provide a remote and non-destructive inspection solution to solid cylinders due to its flexible adaptability to complex structures and strong penetration depth. However, constrained by the common problems of optical detection systems, the detected ultrasonic signals will suffer low signal-to-noise ratio and bad resolution from poor sample surface quality. Thus, a modified synthetic aperture focusing technique (SAFT) optimized for cylindrical components is proposed to improve the detectability of LU on small and buried defects. Mode conversion wave signals obtained by multiple separate excitations are used in SAFT imaging for the reconstruction of the defects under surface profile correction. To reduce the influence of incident waves such as direct surface acoustic wave, besides common difference method, adjacent wave subtraction algorithm based on cross-correlation is used for signal preprocessing, suppressing the incident waves and exaggerating the mode conversion waves. In numerical simulation, internal defects with diameters from 0.6 to 0.2 mm with various buried depths are visualized and accurately located via the optimized SAFT algorithm using mode conversion waves. For validation, a circumferential scanning system is established in LU experiment and internal defects from 0.8 to 0.4 mm in diameter inside solid cylinders are successfully detected with precise location. The results elucidate the reliability of characterizing the location of small internal buried defects in solid cylinder structures through LU-SAFT imaging.
圆柱形元件的健康监测对于工业生产的安全运行尤为重要，因为圆柱形元件承担着大量的支撑和传输工作。普通的无损评估技术需要特殊的设计，以适应圆柱体的高曲率表面。激光超声（LU）方法因其对复杂结构的灵活适应性和较强的穿透深度，可为实心圆柱体提供远程无损检测解决方案。然而，受光学检测系统常见问题的限制，检测到的超声波信号会因样品表面质量差而信噪比低、分辨率低。因此，我们提出了一种针对圆柱形部件进行优化的改良合成孔径聚焦技术（SAFT），以提高对小型和埋藏缺陷的 LU 检测能力。在 SAFT 成像中，通过多个单独激励获得的模式转换波信号用于表面轮廓校正下的缺陷重建。为了减少直接表面声波等入射波的影响，除了采用共差法之外，还采用了基于交叉相关的相邻波减法算法进行信号预处理，抑制入射波，夸大模式转换波。在数值模拟中，通过优化的 SAFT 算法，利用模式转换波对直径为 0.6 至 0.2 毫米、埋深不同的内部缺陷进行可视化和精确定位。为进行验证，在 LU 实验中建立了圆周扫描系统，并成功检测到实心圆柱体内部直径为 0.8 至 0.4 毫米的内部缺陷，并进行了精确定位。这些结果阐明了通过 LU-SAFT 成像表征实心圆柱体结构内部埋藏的小缺陷位置的可靠性。
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Pub Date : 2024-09-21DOI: 10.1016/j.ndteint.2024.103242
Bozhou Zhuang , Bora Gencturk , Anton Sinkov , Morris Good , Ryan Meyer , Assad Oberai
The safe storage of spent nuclear fuel (SNF) in dry cask storage systems (DCSSs) is critical to the nuclear fuel cycle and the future of nuclear energy. A critical component of DCSSs is the SNF canister. The canister is a sealed stainless-steel structure, which is first vacuum dried and then backfilled with helium. The structural deterioration within a canister can be monitored through its internal gas properties. This monitoring serves as the driving force behind the non-invasive ultrasonic sensing approach in this paper. A major challenge in collecting gas-borne signals using ultrasonic sensing is the impedance mismatch between the stainless-steel canister and the helium gas inside. Only a small fraction of the ultrasonic signal makes its way from the transmitter to the receiver through the gas medium. In this paper, experimental studies on a partial full-scale canister mock-up were carried out to capture the gas-borne signals. Damping materials were applied on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The research results showed that the excitation frequency played an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight (TOF) at 225 kHz. A high signal-to-noise ratio (SNR) was achieved in the measurements. Next, acoustic impedance matching (AIM) layers were added, and it was found that the gas signal energy was improved by 160.4% compared with that of no AIM layers. Subsequently, the relative humidity (RH) level and temperature of the gas were varied to simulate abnormal internal conditions of the canister. The non-invasive testing system demonstrated reliability and sensitivity in detecting gas temperature and RH variations. Theoretical calculations demonstrated the potential for detecting low-level xenon and air within an actual SNF canister filled with helium. Last, an active noise cancellation (ANC) method, previously developed by the authors, was verified on the canister mock-up for the first time. The results showed that the SNR of the gas signal was improved by 213.6% compared with that of no ANC.
{"title":"Non-invasive ultrasonic sensing of internal conditions on a partial full-scale spent nuclear fuel canister mock-up","authors":"Bozhou Zhuang , Bora Gencturk , Anton Sinkov , Morris Good , Ryan Meyer , Assad Oberai","doi":"10.1016/j.ndteint.2024.103242","DOIUrl":"10.1016/j.ndteint.2024.103242","url":null,"abstract":"<div><div>The safe storage of spent nuclear fuel (SNF) in dry cask storage systems (DCSSs) is critical to the nuclear fuel cycle and the future of nuclear energy. A critical component of DCSSs is the SNF canister. The canister is a sealed stainless-steel structure, which is first vacuum dried and then backfilled with helium. The structural deterioration within a canister can be monitored through its internal gas properties. This monitoring serves as the driving force behind the non-invasive ultrasonic sensing approach in this paper. A major challenge in collecting gas-borne signals using ultrasonic sensing is the impedance mismatch between the stainless-steel canister and the helium gas inside. Only a small fraction of the ultrasonic signal makes its way from the transmitter to the receiver through the gas medium. In this paper, experimental studies on a partial full-scale canister mock-up were carried out to capture the gas-borne signals. Damping materials were applied on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The research results showed that the excitation frequency played an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight (TOF) at 225 kHz. A high signal-to-noise ratio (SNR) was achieved in the measurements. Next, acoustic impedance matching (AIM) layers were added, and it was found that the gas signal energy was improved by 160.4% compared with that of no AIM layers. Subsequently, the relative humidity (RH) level and temperature of the gas were varied to simulate abnormal internal conditions of the canister. The non-invasive testing system demonstrated reliability and sensitivity in detecting gas temperature and RH variations. Theoretical calculations demonstrated the potential for detecting low-level xenon and air within an actual SNF canister filled with helium. Last, an active noise cancellation (ANC) method, previously developed by the authors, was verified on the canister mock-up for the first time. The results showed that the SNR of the gas signal was improved by 213.6% compared with that of no ANC.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"148 ","pages":"Article 103242"},"PeriodicalIF":4.1,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1016/j.ndteint.2024.103241
Natalie Reed, Joseph Corcoran
Erosion-corrosion is a problematic damage mechanism for the oil and gas industry. To manage the risk of erosion-corrosion networks of particle impact monitoring systems have been installed on pipelines in order to detect acoustic emission from abrasive sand particles impacting the inside surface of the pipe. It would be of value if the existing network of particle impact monitoring systems were not only capable of detecting particle impact, but also sizing the remaining wall thickness. Particle impact monitoring systems are passive and are not generally equipped for excitation. This paper explores the feasibility of using passive acoustic emission transducers for wall thickness measurement, utilizing the fact that active pulse-echo measurements can be approximated by autocorrelating diffuse acoustic waves, such as those generated by particle impact. Two measurement modalities are presented: a) time-of-flight measurements and b) resonant ultrasound spectroscopy measurements. The more usual time-of-flight based measurement is limited by the fact that acoustic emission transducers typically have sensitive bandwidths limited to <1 MHz. The relatively low frequency operation limits the use to thick wall components where the component thickness ≫ ultrasonic wavelength. In thinner walled components a resonant ultrasound spectroscopy approach is required. Experimental measurements are shown that are truly passive (with no purposeful excitation at all), and semi-passive, utilizing acoustic emission from sand impact or compressed air as the excitation source. Results show very good agreement with active measurements.
{"title":"Passive wall thickness monitoring using acoustic emission excitation","authors":"Natalie Reed, Joseph Corcoran","doi":"10.1016/j.ndteint.2024.103241","DOIUrl":"10.1016/j.ndteint.2024.103241","url":null,"abstract":"<div><div>Erosion-corrosion is a problematic damage mechanism for the oil and gas industry. To manage the risk of erosion-corrosion networks of particle impact monitoring systems have been installed on pipelines in order to detect acoustic emission from abrasive sand particles impacting the inside surface of the pipe. It would be of value if the existing network of particle impact monitoring systems were not only capable of detecting particle impact, but also sizing the remaining wall thickness. Particle impact monitoring systems are passive and are not generally equipped for excitation. This paper explores the feasibility of using passive acoustic emission transducers for wall thickness measurement, utilizing the fact that active pulse-echo measurements can be approximated by autocorrelating diffuse acoustic waves, such as those generated by particle impact. Two measurement modalities are presented: a) time-of-flight measurements and b) resonant ultrasound spectroscopy measurements. The more usual time-of-flight based measurement is limited by the fact that acoustic emission transducers typically have sensitive bandwidths limited to <1 MHz. The relatively low frequency operation limits the use to thick wall components where the component thickness ≫ ultrasonic wavelength. In thinner walled components a resonant ultrasound spectroscopy approach is required. Experimental measurements are shown that are truly passive (with no purposeful excitation at all), and semi-passive, utilizing acoustic emission from sand impact or compressed air as the excitation source. Results show very good agreement with active measurements.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"148 ","pages":"Article 103241"},"PeriodicalIF":4.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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