Ndt & E International最新文献

英文中文

Terahertz-based optical parameters analysis and quantitative inclusion defects detection in glass fiber-reinforced polymer laminate

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-15 DOI: 10.1016/j.ndteint.2024.103310

Yu Liu , Yefa Hu , Xinhua Guo , Jinguang Zhang , Xu Xia , Kai Fu

Inclusion defects are often introduced in the manufacturing process of glass fiber-reinforced polymer (GFRP) components, which can be effectively identified by Terahertz (THz) technology. However, accurate measurement of the size and location of defects for engineering applications remains a challenge. In this study, based on the optical parameters of GFRP laminate, a quantitative detection of inclusion defects was conducted. For defect area measurement, a defect area measurement algorithm based on super-resolution generative adversarial network (DAMSRGAN) was proposed, enhancing measurement accuracy by employing generative adversarial networks to improve image resolution. The final quantification of defect area was achieved through a combination of threshold segmentation and blob analysis. Compared to traditional methods for characterizing defect areas based on raw low-resolution time-of-flight tomography (TOFT) images, the proposed algorithm effectively enhances measurement accuracy. For defect depth measurement, the influence of the number of layers and ply angles of GFRP laminates on THz optical parameters was studied, revealing an approximate linear relationship between the number of layers and refractive index of GFRP laminates. Based on this relationship, the refractive index of the tested GFRP sample can be estimated, thereby eliminating the need to remove it from the assembled structure for optical parameter measurement. Furthermore, defect depth information can be calculated based on the estimated refractive index, enhancing the convenience of detecting GFRP defect depth using THz technology. This study provides a valuable supplement for the accurate and convenient measurement of inclusion defects in GFRP components using THz technology.

{"title":"Terahertz-based optical parameters analysis and quantitative inclusion defects detection in glass fiber-reinforced polymer laminate","authors":"Yu Liu , Yefa Hu , Xinhua Guo , Jinguang Zhang , Xu Xia , Kai Fu","doi":"10.1016/j.ndteint.2024.103310","DOIUrl":"10.1016/j.ndteint.2024.103310","url":null,"abstract":"<div><div>Inclusion defects are often introduced in the manufacturing process of glass fiber-reinforced polymer (GFRP) components, which can be effectively identified by Terahertz (THz) technology. However, accurate measurement of the size and location of defects for engineering applications remains a challenge. In this study, based on the optical parameters of GFRP laminate, a quantitative detection of inclusion defects was conducted. For defect area measurement, a defect area measurement algorithm based on super-resolution generative adversarial network (DAMSRGAN) was proposed, enhancing measurement accuracy by employing generative adversarial networks to improve image resolution. The final quantification of defect area was achieved through a combination of threshold segmentation and blob analysis. Compared to traditional methods for characterizing defect areas based on raw low-resolution time-of-flight tomography (TOFT) images, the proposed algorithm effectively enhances measurement accuracy. For defect depth measurement, the influence of the number of layers and ply angles of GFRP laminates on THz optical parameters was studied, revealing an approximate linear relationship between the number of layers and refractive index of GFRP laminates. Based on this relationship, the refractive index of the tested GFRP sample can be estimated, thereby eliminating the need to remove it from the assembled structure for optical parameter measurement. Furthermore, defect depth information can be calculated based on the estimated refractive index, enhancing the convenience of detecting GFRP defect depth using THz technology. This study provides a valuable supplement for the accurate and convenient measurement of inclusion defects in GFRP components using THz technology.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"151 ","pages":"Article 103310"},"PeriodicalIF":4.1,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094576","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}

引用次数: 0

Ultrasonic defect detection in a concrete slab assisted by physics-informed neural networks

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-14 DOI: 10.1016/j.ndteint.2024.103311

Sangmin Lee , John S. Popovics

Traditional nondestructive testing (NDT) methods face challenges to accurately assess concrete owing to its naturally inhomogeneous nature that complicates spatial characterization of material properties. To address these limitations, this work considers physics-informed neural networks (PINNs) interpreting contactless ultrasonic scan data to enhance defect detection capabilities in concrete. PINNs integrate physics laws through mathematical governing equations into artificial neural network models to overcome limitations of purely data-driven analysis approaches. The study utilizes experimental data collected from a large-scale concrete slab containing inclusion, cold joints with cracks, and surface fire damage and from a homogeneous PMMA slab (as a reference). The PINN results are used to create space-dependent property maps based on the extracted coefficient of the governing wave equation using a simple time-domain wavefield data set. The results demonstrate that PINNs effectively predict space-dependent wave velocities. This approach facilitates accurate material property characterization and defect identification. The proposed PINN models achieved a P-wave velocity prediction error of 0.34 % for the PMMA slab and identified areal extent of defects in the concrete slab with errors of 1 % for pristine areas and 2.1 % for inclusion areas. Sub-wavelength-sized cracks around the inclusion areas were detected from the predicted wave velocity map. These findings suggest that PINNs offer a promising approach for improving the accuracy and efficiency of defect detection in concrete structures with superior spatial resolution provided by other conventional ultrasonic imaging approaches.

{"title":"Ultrasonic defect detection in a concrete slab assisted by physics-informed neural networks","authors":"Sangmin Lee , John S. Popovics","doi":"10.1016/j.ndteint.2024.103311","DOIUrl":"10.1016/j.ndteint.2024.103311","url":null,"abstract":"<div><div>Traditional nondestructive testing (NDT) methods face challenges to accurately assess concrete owing to its naturally inhomogeneous nature that complicates spatial characterization of material properties. To address these limitations, this work considers physics-informed neural networks (PINNs) interpreting contactless ultrasonic scan data to enhance defect detection capabilities in concrete. PINNs integrate physics laws through mathematical governing equations into artificial neural network models to overcome limitations of purely data-driven analysis approaches. The study utilizes experimental data collected from a large-scale concrete slab containing inclusion, cold joints with cracks, and surface fire damage and from a homogeneous PMMA slab (as a reference). The PINN results are used to create space-dependent property maps based on the extracted coefficient of the governing wave equation using a simple time-domain wavefield data set. The results demonstrate that PINNs effectively predict space-dependent wave velocities. This approach facilitates accurate material property characterization and defect identification. The proposed PINN models achieved a P-wave velocity prediction error of 0.34 % for the PMMA slab and identified areal extent of defects in the concrete slab with errors of 1 % for pristine areas and 2.1 % for inclusion areas. Sub-wavelength-sized cracks around the inclusion areas were detected from the predicted wave velocity map. These findings suggest that PINNs offer a promising approach for improving the accuracy and efficiency of defect detection in concrete structures with superior spatial resolution provided by other conventional ultrasonic imaging approaches.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"151 ","pages":"Article 103311"},"PeriodicalIF":4.1,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094683","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}

引用次数: 0

Eddy current thermography detection method for internal thickness reduction in ferromagnetic components based on magnetic permeability perturbation

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-13 DOI: 10.1016/j.ndteint.2024.103313

Zhiyang Deng , Zhilong Li , Nan Yang , Jianbo Wu , Xiaochun Song , Yihua Kang

Eddy current thermography (ECT), as an emerging nondestructive testing (NDT) technique, has been used for defect detection in many critical components. However, the skinning effect of eddy currents limits the ability of ECT to detect internal defects in thick-walled pipes. An ECT detection method for thickness reduction of ferromagnetic components based on magnetic permeability perturbation (MPP-ECT) under DC magnetization is proposed. The thickness reduction cause MPP phenomenon on the surface of ferromagnetic components. Then under high-frequency AC excitation, the thinning area affected by MPP will produce a different thermal response from the normal area, which is recognized and captured by an infrared camera. The mechanism of MPP-based thinning defect detection is analyzed through a theoretical model, and the relationship between thinning thickness, relative permeability and thermal response is established. The feasibility of the MPP-ECT detection method is verified through a series of simulations and experiments. The experimental results show that the method can effectively detect the thinning defect of 4.2 % wall thickness on the back of 12 mm thick specimens. The thermal response of both the thinning and normal areas decreases with increasing magnetization intensity, and the thermal response of the thinning area decreases with increasing thinning thickness. However, the thermal contrast (peak-to-peak value of thermal response) between the two regions increases with the increase of magnetization intensity and thinning thickness. This method can be used for detection under high lift off and weakens the skin effect of ECT for the internal thickness reduction, which has great practical value.

{"title":"Eddy current thermography detection method for internal thickness reduction in ferromagnetic components based on magnetic permeability perturbation","authors":"Zhiyang Deng , Zhilong Li , Nan Yang , Jianbo Wu , Xiaochun Song , Yihua Kang","doi":"10.1016/j.ndteint.2024.103313","DOIUrl":"10.1016/j.ndteint.2024.103313","url":null,"abstract":"<div><div>Eddy current thermography (ECT), as an emerging nondestructive testing (NDT) technique, has been used for defect detection in many critical components. However, the skinning effect of eddy currents limits the ability of ECT to detect internal defects in thick-walled pipes. An ECT detection method for thickness reduction of ferromagnetic components based on magnetic permeability perturbation (MPP-ECT) under DC magnetization is proposed. The thickness reduction cause MPP phenomenon on the surface of ferromagnetic components. Then under high-frequency AC excitation, the thinning area affected by MPP will produce a different thermal response from the normal area, which is recognized and captured by an infrared camera. The mechanism of MPP-based thinning defect detection is analyzed through a theoretical model, and the relationship between thinning thickness, relative permeability and thermal response is established. The feasibility of the MPP-ECT detection method is verified through a series of simulations and experiments. The experimental results show that the method can effectively detect the thinning defect of 4.2 % wall thickness on the back of 12 mm thick specimens. The thermal response of both the thinning and normal areas decreases with increasing magnetization intensity, and the thermal response of the thinning area decreases with increasing thinning thickness. However, the thermal contrast (peak-to-peak value of thermal response) between the two regions increases with the increase of magnetization intensity and thinning thickness. This method can be used for detection under high lift off and weakens the skin effect of ECT for the internal thickness reduction, which has great practical value.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"151 ","pages":"Article 103313"},"PeriodicalIF":4.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094677","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}

引用次数: 0

Pipeline integrity gauges based on dynamic magnetic coupling sensing technology

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-11 DOI: 10.1016/j.ndteint.2024.103307

Gaige Ru , Bin Gao , Songwen Xue , Jun Xian , Yuxi Xie , Wai Lok Woo

This paper proposes a novel sensing system for in-line-inspection of pipelines, based on dynamic coupled of integrating the magnetic perturbation with motive induced eddy current. This approach simultaneously addresses the key challenges of high energy-consumption as well as the detection of multi-types of defects. The sensing characteristics involves a novel probe structure incorporating a detection coil and ring-magnetic source, capable of identifying different defects at varying speed. In particular, the motion-induced eddy current can be theoretically modeled by the relative motion between the magnet and the pipe. Interpretation of both distribution and perturbations of eddy currents at different speeds is detail discussed. The internal and external receiving coils can capture information on magnetic perturbation and eddy currents disturbances, effectively elucidating the impact of the probe velocity. Finally, the superiority of the proposed system was validated through simulation, experimental verification, and real pipe pulling testing.

引用次数: 0

Improving automatic defect recognition on GDXRay castings dataset by introducing GenAI synthetic training data

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-11 DOI: 10.1016/j.ndteint.2024.103303

A. García-Pérez , M.J. Gómez-Silva , A. de la Escalera-Hueso

X-rays are a Non Destructive Testing (NDT) technique commonly employed by aerospace, automotive or nuclear industries when the structural integrity of some parts needs to be guaranteed. Industrial dataset are now available with the introduction of Digital Radiography (DR) X-ray machine and are the basis for Automated Defect Recognition (ADR) systems based on Neural Network (NN) object detection models. However, building a big enough dataset is not easy and takes a long time in a production environment, delaying the introduction of ADR models. A potential solution is to use Generative Artificial Intelligence (GenAI) to synthesise new images. However, these models fail to generate full realistic images due to the subtle nature of X-ray images. Hence, this paper propose a combination of flawless images and synthetic defects generated by a novel Scalable Conditional Wasserstein GAN (SCWGAN) model. Such synthetic defects are introduced in the target images by a location algorithm that uses a mask image defining the allowable defective areas, the expected Gaussian or Poisson noise level and the defect size and aspect ratio. By creating such synthetic dataset and combine it with the original GDXRay dataset, our proposed detection system achieves an improvement of 17 % in mAP@IoU=0.5:0.95 (our target metric to reduced uncertainty on defect location) with regards the baseline model trained with only real images. As a secondary metric, to allow comparison with other studies, the model also achieves 96.0 % mAP@IoU=0.50, which exceeds the maximum accuracy available on current literature for the evaluated dataset.

{"title":"Improving automatic defect recognition on GDXRay castings dataset by introducing GenAI synthetic training data","authors":"A. García-Pérez , M.J. Gómez-Silva , A. de la Escalera-Hueso","doi":"10.1016/j.ndteint.2024.103303","DOIUrl":"10.1016/j.ndteint.2024.103303","url":null,"abstract":"<div><div>X-rays are a Non Destructive Testing (NDT) technique commonly employed by aerospace, automotive or nuclear industries when the structural integrity of some parts needs to be guaranteed. Industrial dataset are now available with the introduction of Digital Radiography (DR) X-ray machine and are the basis for Automated Defect Recognition (ADR) systems based on Neural Network (NN) object detection models. However, building a big enough dataset is not easy and takes a long time in a production environment, delaying the introduction of ADR models. A potential solution is to use Generative Artificial Intelligence (GenAI) to synthesise new images. However, these models fail to generate full realistic images due to the subtle nature of X-ray images. Hence, this paper propose a combination of flawless images and synthetic defects generated by a novel Scalable Conditional Wasserstein GAN (SCWGAN) model. Such synthetic defects are introduced in the target images by a location algorithm that uses a mask image defining the allowable defective areas, the expected Gaussian or Poisson noise level and the defect size and aspect ratio. By creating such synthetic dataset and combine it with the original GDXRay dataset, our proposed detection system achieves an improvement of 17% in mAP@IoU=0.5:0.95 (our target metric to reduced uncertainty on defect location) with regards the baseline model trained with only real images. As a secondary metric, to allow comparison with other studies, the model also achieves 96.0% mAP@IoU=0.50, which exceeds the maximum accuracy available on current literature for the evaluated dataset.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"151 ","pages":"Article 103303"},"PeriodicalIF":4.1,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093674","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}

引用次数: 0

An automatic welding defect detection method based on deep learning for super 8-bit high grayscale X-ray films of solid rocket motor shells

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-06 DOI: 10.1016/j.ndteint.2024.103306

Peng Wang , Liangliang Li , Xiaoyan Li , Leiguang Duan , Zhigang Lü , Ruohai Di

The solid rocket motor has a wide range of applications in military weapons and model rockets. The shell is the main component, which is the welding and long-term operation, some defects will inevitably appear, which directly affect the performance of the solid rocket motor. This paper aims to solve the visual enhancement and defect detection of X-ray film of solid engine shells with unbalanced brightness and contrast and indistinct details in dark parts. To solve the problem that high grayscale RAW images cannot be displayed normally on low-bit monitors, an adaptive enhancement algorithm based on the high grayscale image is proposed. Further, to improve the observability of detailed information, a pseudo-color enhancement algorithm based on multi-chromatographic space fusion and controllable brightness is proposed. In addition, we constructed a new small sample dataset for super-8-bit welding defect detection and an object detection model that can be used to identify super-8-bit welding defects. The experimental results show that the method designed in this paper can effectively improve defect recognition in high grayscale RAW images, and can better detect defect types. In addition, we try to implement a texture mapping based 3D surface image rendering method and apply the 2D defect detection method to the 3D rendering image, which has a good detection performance and provides an effective idea for the 3D rendering of welding defects and surface defects detection.

{"title":"An automatic welding defect detection method based on deep learning for super 8-bit high grayscale X-ray films of solid rocket motor shells","authors":"Peng Wang , Liangliang Li , Xiaoyan Li , Leiguang Duan , Zhigang Lü , Ruohai Di","doi":"10.1016/j.ndteint.2024.103306","DOIUrl":"10.1016/j.ndteint.2024.103306","url":null,"abstract":"<div><div>The solid rocket motor has a wide range of applications in military weapons and model rockets. The shell is the main component, which is the welding and long-term operation, some defects will inevitably appear, which directly affect the performance of the solid rocket motor. This paper aims to solve the visual enhancement and defect detection of X-ray film of solid engine shells with unbalanced brightness and contrast and indistinct details in dark parts. To solve the problem that high grayscale RAW images cannot be displayed normally on low-bit monitors, an adaptive enhancement algorithm based on the high grayscale image is proposed. Further, to improve the observability of detailed information, a pseudo-color enhancement algorithm based on multi-chromatographic space fusion and controllable brightness is proposed. In addition, we constructed a new small sample dataset for super-8-bit welding defect detection and an object detection model that can be used to identify super-8-bit welding defects. The experimental results show that the method designed in this paper can effectively improve defect recognition in high grayscale RAW images, and can better detect defect types. In addition, we try to implement a texture mapping based 3D surface image rendering method and apply the 2D defect detection method to the 3D rendering image, which has a good detection performance and provides an effective idea for the 3D rendering of welding defects and surface defects detection.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"151 ","pages":"Article 103306"},"PeriodicalIF":4.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093672","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}

引用次数: 0

Interactive defect segmentation in welding radiographic images based on artificial features fusion

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-06 DOI: 10.1016/j.ndteint.2024.103305

Z.H. Yan , B.W. Ji , H. Xu , J. Fang

In recent years, deep learning technology has been used in the defect detection of weld radiographic images with its rapid development. However, there are several questions need to be solved for the wide application of deep learning technology in engineering. First, the lack of prior information due to the lack of large number of training data limits the performance of the model; Secondly, it takes too long for the labeling work of manual discrimination. In addition, when the deep learning prediction is wrong, it is very difficult for human intervention to correct. To solve these problems, a human-computer interaction method for weld defect detection based on HRNet + OCR deep learning model was suggested in this work. In the data set preparation stage, different from the previous processing methods, this paper eliminates the pure background images that do not contain instances, and then not only segmenting the defects in the weld images, but also making different labeling maps for different types of defects and pseudo-defects respectively, solving the problem that the network pays too much attention to the semantic information of the image while ignoring the user interaction when predicting was solved. In the artificial feature extraction phase, based on human experience, the ray image is processed to enhance the non-equilibrium region in the image, especially the non-equilibrium region with small size and weak intensity. Artificial features were integrated into the network, to obtain a stronger and more robust ability to focus and extract the unbalanced areas in the image, this paper proposes to artificial features. The experimental results showed that the best performance of the network can be achieved when the artificial feature convolution kernel with foreground scale of 3 pixels, background scales of 15 pixels and 31 pixels is used in the test data. Through this method, the model can achieve 2.30 and 3.67 in Noc@75 and Noc@80, compared to the model without fusion of artificial features which improves 68.7 % and 64.3 % in Noc@75 and Noc@80, respectively.

{"title":"Interactive defect segmentation in welding radiographic images based on artificial features fusion","authors":"Z.H. Yan , B.W. Ji , H. Xu , J. Fang","doi":"10.1016/j.ndteint.2024.103305","DOIUrl":"10.1016/j.ndteint.2024.103305","url":null,"abstract":"<div><div>In recent years, deep learning technology has been used in the defect detection of weld radiographic images with its rapid development. However, there are several questions need to be solved for the wide application of deep learning technology in engineering. First, the lack of prior information due to the lack of large number of training data limits the performance of the model; Secondly, it takes too long for the labeling work of manual discrimination. In addition, when the deep learning prediction is wrong, it is very difficult for human intervention to correct. To solve these problems, a human-computer interaction method for weld defect detection based on HRNet + OCR deep learning model was suggested in this work. In the data set preparation stage, different from the previous processing methods, this paper eliminates the pure background images that do not contain instances, and then not only segmenting the defects in the weld images, but also making different labeling maps for different types of defects and pseudo-defects respectively, solving the problem that the network pays too much attention to the semantic information of the image while ignoring the user interaction when predicting was solved. In the artificial feature extraction phase, based on human experience, the ray image is processed to enhance the non-equilibrium region in the image, especially the non-equilibrium region with small size and weak intensity. Artificial features were integrated into the network, to obtain a stronger and more robust ability to focus and extract the unbalanced areas in the image, this paper proposes to artificial features. The experimental results showed that the best performance of the network can be achieved when the artificial feature convolution kernel with foreground scale of 3 pixels, background scales of 15 pixels and 31 pixels is used in the test data. Through this method, the model can achieve 2.30 and 3.67 in Noc@75 and Noc@80, compared to the model without fusion of artificial features which improves 68.7 % and 64.3 % in Noc@75 and Noc@80, respectively.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"151 ","pages":"Article 103305"},"PeriodicalIF":4.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094574","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}

引用次数: 0

Non-destructive procedure to determine residual stresses and white layers in hole making operations

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-05 DOI: 10.1016/j.ndteint.2024.103304

Aitor Madariaga , Gorka Ortiz-de-Zarate , Pedro J. Arrazola

Holes are one of the most critical features of aero-engine components subjected to fatigue loads. Thus, it is essential to ensure a good surface integrity during hole making operations. This work proposes a non-destructive procedure based on X-ray diffraction measurements to determine residual stresses and white layers in holes. Drilling tests were done in Inconel 718 using new and worn tools for different cutting conditions. The results showed that residual stresses can be determined non-destructively with ±150 MPa error. Importantly, Full Width at Half Maximum values showed an unequivocal agreement with the presence of white layer and plastic deformation.

引用次数: 0

A weak bias-magnetized dynamic permeability testing method for detecting and distinguishing inner and outer diameter defects in gas pipelines

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-03 DOI: 10.1016/j.ndteint.2024.103284

Zhengyu Ou , Zhihao Shu , Tianyun He , Cheng Xu , Jisong Cen , Zandong Han

Detecting and distinguishing inner diameter (ID) and outer diameter (OD) defects in gas pipelines is crucial for their safe operation. However, current methods such as eddy current testing (ECT) and saturated magnetic flux leakage (MFL) are unable to meet the detection requirements due to the small diameter and low pressure of gas pipelines. Therefore, this paper presents a weak bias-magnetized dynamic permeability testing (DPT) method, using a differential probe with AC coils and magnetic cores to sense varying physical quantities in the ID surface caused by defects. For ID defects, the varying physical quantities include both dynamic permeability and conductivity, whereas for OD defects, it is solely dynamic permeability. This paper thoroughly analyzes the detection principle and completes the coil impedance model. A simulation model for small AC electromagnetic fields with DC bias is established, and an impedance detection instrument is developed for further experimental investigation. The results show that the weak bias-magnetized DPT can detect both ID and OD defects and distinguish them based on clear differences in the signal waveform. Compared to ECT and MFL, the DPT method has the highest ratio of OD to ID defect signal magnitude, indicating minimal signal attenuation as the buried depth of defects increases. Moreover, a pipeline internal inspection device based on DPT has been developed, demonstrating its capability to detect OD defects with a small drag force.

{"title":"A weak bias-magnetized dynamic permeability testing method for detecting and distinguishing inner and outer diameter defects in gas pipelines","authors":"Zhengyu Ou , Zhihao Shu , Tianyun He , Cheng Xu , Jisong Cen , Zandong Han","doi":"10.1016/j.ndteint.2024.103284","DOIUrl":"10.1016/j.ndteint.2024.103284","url":null,"abstract":"<div><div>Detecting and distinguishing inner diameter (ID) and outer diameter (OD) defects in gas pipelines is crucial for their safe operation. However, current methods such as eddy current testing (ECT) and saturated magnetic flux leakage (MFL) are unable to meet the detection requirements due to the small diameter and low pressure of gas pipelines. Therefore, this paper presents a weak bias-magnetized dynamic permeability testing (DPT) method, using a differential probe with AC coils and magnetic cores to sense varying physical quantities in the ID surface caused by defects. For ID defects, the varying physical quantities include both dynamic permeability and conductivity, whereas for OD defects, it is solely dynamic permeability. This paper thoroughly analyzes the detection principle and completes the coil impedance model. A simulation model for small AC electromagnetic fields with DC bias is established, and an impedance detection instrument is developed for further experimental investigation. The results show that the weak bias-magnetized DPT can detect both ID and OD defects and distinguish them based on clear differences in the signal waveform. Compared to ECT and MFL, the DPT method has the highest ratio of OD to ID defect signal magnitude, indicating minimal signal attenuation as the buried depth of defects increases. Moreover, a pipeline internal inspection device based on DPT has been developed, demonstrating its capability to detect OD defects with a small drag force.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"151 ","pages":"Article 103284"},"PeriodicalIF":4.1,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093675","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}

引用次数: 0

The multi-mode reverse time migration for defect characterization using ultrasonic array

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2024-12-03 DOI: 10.1016/j.ndteint.2024.103293

Abhishek Saini , Jinwei Fang , Huaigu Tang

An ultrasonic wavefield-based imaging technique, termed Multi-Mode Reverse-Time Migration (MMRTM) is implemented for characterizing complex-shaped surface-breaking cracks. This method enables the imaging and sizing such cracks by combining longitudinal (L) and/or shear (S) wave-mode images. The MMRTM builds upon the Reverse-Time Migration (RTM) by separating the wavefield into multiple modes prior to imaging. A 2D imaging approach for isotropic elastic materials is developed, involving the decomposition of source and receiver wavefields into L-and S-wave vectors using decoupled elastodynamic extrapolation. This method preserves the original stress and particle velocity components, ensuring accurate amplitude and phase information in the separated wavefields. Inner-product imaging condition is then applied to generate LL, LS, and SS images of surface-breaking cracks. Both simulations and experimental validations are conducted to showcase the efficacy of MMRTM in characterizing defects. Furthermore, the MMRTM is compared with the Total Focusing Method and conventional RTM, where it shows significant improvement in image reconstruction compared to the other two imaging methods. The results demonstrate its potential utility in evaluating various complex-shaped defects, including fatigue cracks and stress corrosion cracks.

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