Journal of Nondestructive Evaluation最新文献_第8页

A Novel Detail Enhancement Method for Industrial Digital Radiography Based on Multiscale Pixel-Level Adaptive Fusion 基于多尺度像素级自适应融合的工业数字射线成像细节增强方法

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-02 DOI: 10.1007/s10921-025-01197-7

Guancheng Lu, Juan Huang

{"title":"A Novel Detail Enhancement Method for Industrial Digital Radiography Based on Multiscale Pixel-Level Adaptive Fusion","authors":"Guancheng Lu, Juan Huang","doi":"10.1007/s10921-025-01197-7","DOIUrl":"10.1007/s10921-025-01197-7","url":null,"abstract":"<div><p>To address the limitations of current methods, such as detail loss, difficulties in enhancing complex features, reliance on powerful computing systems, and difficulties in engineering applications, a novel detail enhancement method for X-ray images in industrial digital radiography is proposed based on multiscale pixel-level adaptive fusion in logarithmic space. In the proposed method, Gaussian convolution is used to construct a multiscale space of the X-ray image. Based on the fact that Gaussian blur and detail enhancement exhibit inverse relationships, the pixel importance for detail enhancement is assessed by the difference between the Gaussian convolved image and the original image. The tanh function and pixel importance for detail enhancement are employed to infer the pixel fusion coefficient, and an enhancement method for X-ray images is achieved through pixel-level adaptive fusion across scales in logarithmic space. The experimental results show that in terms of PSNR, the proposed method improves HE, CLAHE, LCR, DWT, TC-U-NET, and CNN-DEMD by an average of 50.98%, 43.78%, 28.38%, 26.52%, 7.93%, and 5.50%, respectively. The proposed method improves HE, CLAHE, LCR, DWT, TC-U-NET, and CNN-DEMD by an average of 54.68%, 41.40%, 21.95%, 18.65%, 7.04%, and 4.58%, respectively. In terms of SF, the proposed method increases HE, CLAHE, LCR, DWT, TC-U-NET, and CNN-DEMD by an average of 44.48%, 33.25%, 19.56%, 20.13%, 6.17%, and 4.85%, respectively. The experimental findings demonstrate that the proposed method achieves favorable results and exhibits excellent performance.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161035","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

Research on X-ray Image Fusion Algorithm for Food Foreign Object Detection 食品异物检测中的x射线图像融合算法研究

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-02 DOI: 10.1007/s10921-025-01203-y

Jianfeng Yao, Zhenyang Wu, Pengtao Wang, Junchao Ye, Jingxian Wang

{"title":"Research on X-ray Image Fusion Algorithm for Food Foreign Object Detection","authors":"Jianfeng Yao, Zhenyang Wu, Pengtao Wang, Junchao Ye, Jingxian Wang","doi":"10.1007/s10921-025-01203-y","DOIUrl":"10.1007/s10921-025-01203-y","url":null,"abstract":"<div><p>In the field of non-destructive testing of foreign objects in food, the high cost and low efficiency of manual labeling greatly limit the application of X-ray foreign object detection systems. To overcome this problem, this paper proposes a technique for fusing foreign object images with food images, and the fused images enable the automatic labeling of foreign objects. Firstly, X-ray images of foreign objects and food images were collected, and data augmentation was performed on the foreign object images to increase their diversity. Then the food images were fused with the enhanced foreign object images, and the foreign objects were automatically labeled in the fused food images. Finally, the foreign object detection models Model_Y2 and Model_Y1 were established using the dataset automatically annotated by the image fusion method and the dataset manually collected and annotated by traditional methods. The results demonstrate that the proposed method substantially decreases annotation time by 90% while concurrently improving annotation efficiency and accuracy. Comparatively, Model_Y2 outperforms Model_Y1 with a 4.5% higher mAP@0.5:0.95. This indicates that the method not only enhances data annotation efficiency and quality but also improves the accuracy of X-ray foreign object detection, providing a highly efficient and practical technical solution for the intelligent development of food safety inspection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161138","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

Point-to-Point Dispersion Estimation of Lamb Waves Lamb波的点对点色散估计

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-02 DOI: 10.1007/s10921-025-01200-1

Yu Ruan, Zhibo Yang, Yanfeng Lang, Donglong He, Minyue Lu, Xuefeng Chen

{"title":"Point-to-Point Dispersion Estimation of Lamb Waves","authors":"Yu Ruan, Zhibo Yang, Yanfeng Lang, Donglong He, Minyue Lu, Xuefeng Chen","doi":"10.1007/s10921-025-01200-1","DOIUrl":"10.1007/s10921-025-01200-1","url":null,"abstract":"<div><p>This paper presents a methodology for estimating group velocities of Lamb waves without requiring prior knowledge of material properties. First, inspired by the FRF approach, the concept of Pseudo time-frequency distribution (PTFD) is introduced to identify the wave component over a relatively wide frequency range from a single measurement. Next, the MDPORE method is used to detect the Time-of-Arrival (ToA) ridge of each mode. The ToA ridge is then employed to calculate the group-velocities, which are further smoothed using an S-G filter. The calculated results are then converted into a wavenumber representation to determine the reference time span of the target mode wave packet at a given distance. Based on this, the direct wave component is extracted from the response and used to calculate the confidence index, which assess the non-linear spectrum modulation of the travelling wave packet. Multiple PTFDs, each with different cycle numbers tone burst excitation, are employed to perform ridge detection for group velocity calculation and confidence level evaluation. Finally, the group-velocity result corresponding to the maximum confidence level is selected as the final result at each frequency point. Numerical and experimental responses of aluminum plates are used to validate the feasibility and robustness of the proposed dispersion estimation method.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161139","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

Influence of Layer Thickness and Disc Orientation on Additively Manufactured Disc-Shaped Reflectors for Ultrasonic Testing 层厚和圆盘方向对增材制造的超声检测圆盘形反射器的影响

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-02 DOI: 10.1007/s10921-025-01202-z

Maria Grozdanić, Morana Mihaljević, Hrvoje Cajner, Damir Godec

{"title":"Influence of Layer Thickness and Disc Orientation on Additively Manufactured Disc-Shaped Reflectors for Ultrasonic Testing","authors":"Maria Grozdanić, Morana Mihaljević, Hrvoje Cajner, Damir Godec","doi":"10.1007/s10921-025-01202-z","DOIUrl":"10.1007/s10921-025-01202-z","url":null,"abstract":"<div><p>This research investigates the impact of additive manufacturing (AM) parameters on the quality of disc-shaped reflectors (DSRs) embedded within test specimens for ultrasonic testing (UT). Using Direct Metal Laser Sintering (DMLS) technology on MS1 Maraging Steel, the study focuses on two critical AM parameters influencing the staircase effect: layer thickness and disc orientation. These factors affect the dimensional accuracy, shape, and surface quality of the additively manufactured discs. Discs of 1 mm and 2 mm diameter were produced and evaluated for their suitability as reference reflectors for setting the sensitivity of UT systems. A factorial design of experiments (DOE) was applied to quantify the effects of these parameters on the equivalent reflector size (ERS) of discs using the Distance-Gain-Size (DGS) technique. Results show that disc orientation is the dominant factor affecting ultrasonic response, and consequently, ERS value, with optimal results achieved when discs are embedded in the layer direction (0°). The influence of layer thickness is more pronounced for 1 mm discs, where reducing layer thickness to 20 μm enhances reproducibility. For 2 mm discs, the effect of layer thickness is minimal, allowing for cost-effective production with 50 μm layer thickness. These findings provide guidance for optimising AM process parameters to produce high-quality additively manufactured DSRs, contributing to setting the sensitivity of the UT system.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nondestructive Evaluation of the Ductile to Brittle Transition Temperature Based on Magnetic Domain Wall Dynamics and Dislocation Dynamics 基于磁畴壁动力学和位错动力学的韧脆转变温度无损评价

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-02 DOI: 10.1007/s10921-025-01198-6

Ji-Pei Chen, Tao Zhang, Tie-Long Shen, Xiang-Bing Liu

{"title":"Nondestructive Evaluation of the Ductile to Brittle Transition Temperature Based on Magnetic Domain Wall Dynamics and Dislocation Dynamics","authors":"Ji-Pei Chen, Tao Zhang, Tie-Long Shen, Xiang-Bing Liu","doi":"10.1007/s10921-025-01198-6","DOIUrl":"10.1007/s10921-025-01198-6","url":null,"abstract":"<div><p>There has been an increasing interest in development of nondestructive evaluation (NDE) methods to detect the irradiation embrittlement of key components in nuclear reactor. In this paper, a NDE method was developed by testing the temperature (<i>T</i>) dependence of magnetization (<i>M</i>) and damping factor (tan δ) in three different alloys steels. The experimental results show a characteristic temperature in <i>M</i>-<i>T</i> curve and tan δ<i>-T</i> curve, respectively, which coincides well with the ductile–brittle transition temperature (DBTT) measured by Charpy impact test. In <i>M</i>-<i>T</i> curve, the phenomenon is closely related to magnetic domain wall (DW) motion state in the ductile and brittle regions, which is reflected by the motion of dislocations. To explain these observations, a simple model of magnetic DW state in the nanowire with a single defect was built and studied by employing micromagnetic simulation. Simulated results revealed the thermally activated DW depinning and displacement, which can be well characterized by <i>T</i>-dependent magnetization. Furthermore, an analytical model of stochastic dynamical DW was proposed to interpret the <i>T</i>-dependent displacements and magnetization of DW, with some formulae derived for these dependences in an analytical approach. The analytical results show consistence with the simulated data, suggesting that thermally activated DW depinning and motion is responsible for the temperature dependence of magnetization in martensitic steels. The present work is instructive to understand the underlying correlation between embrittlement and thermal dynamics of DW in martensitic steels, and also provides a promising NDE technique to estimate DBTT by tracking the temperature dependence of DW motion and magnetization.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161135","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

Evaluating the Reliability of Carbon-Fiber Composite Through Applying a Tree-Based-Ensemble Method to Analyze Acoustic-Emission Signals 用基于树的集成方法分析声发射信号评估碳纤维复合材料可靠性

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-02 DOI: 10.1007/s10921-025-01209-6

Selma Tchoketch-Kebir, Redouane Drai, Nawal Cheggaga, Nihed-Souhila Alloui, Chems-Eddine-Haithem Taia, Walid Bouali, Ahmed Kechida

{"title":"Evaluating the Reliability of Carbon-Fiber Composite Through Applying a Tree-Based-Ensemble Method to Analyze Acoustic-Emission Signals","authors":"Selma Tchoketch-Kebir, Redouane Drai, Nawal Cheggaga, Nihed-Souhila Alloui, Chems-Eddine-Haithem Taia, Walid Bouali, Ahmed Kechida","doi":"10.1007/s10921-025-01209-6","DOIUrl":"10.1007/s10921-025-01209-6","url":null,"abstract":"<div><p>This paper outlines the creation of a non-destructive testing (NDT) technique that incorporates advanced machine learning (ML) methodologies to enhance damage detection and diagnosis capabilities in various industrial sectors. The selected NDT approach for identifying damage in composite structures is based on acoustic emission (AE) principles. The diagnosis process involves the analysis of acoustic signals obtained through a rigorous experimental procedure. A tree-based-ensemble technique, rooted in ML methods, was employed to process the acquired acoustic dataset. The application of this tree-based-ensemble technique in the diagnosis of composite structures consists of two critical steps. The first step entails the collection of an experimental dataset that provides a comprehensive AE-based dataset for a specific composite structure sample, particularly one composed of carbon fiber (CF) composite. The second step includes the signal processing of the collected dataset utilizing the tree-based-ensemble technique. The results generated from this ensemble-based approach demonstrated significant performance when compared to the diagnosis outcomes produced by the Vallen-AE suite software for data acquisition. Besides, it outperforms other ML-based methods in terms of many metrics evaluated. This developed methodology offers innovative and effective solutions for quality inspection across various industrial applications, achieving an accuracy rate exceeding 96%.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161137","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

Surface Defect Detection of Ultrathin Fiberboard Based on Improved YOLOv8x 基于改进YOLOv8x的超薄纤维板表面缺陷检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-29 DOI: 10.1007/s10921-025-01196-8

Yang Long, Wenshu Lin

{"title":"Surface Defect Detection of Ultrathin Fiberboard Based on Improved YOLOv8x","authors":"Yang Long, Wenshu Lin","doi":"10.1007/s10921-025-01196-8","DOIUrl":"10.1007/s10921-025-01196-8","url":null,"abstract":"<div><p>Due to significant variations in scale and the predominance of which are small-scale surface defects in Ultrathin Fiberboard (UTFB), manual visual detection remains the primary detection method. However, the efficiency and accuracy of manual visual detection are insufficient to meet the demands of modern UTFB production. Therefore, an improved YOLOv8x algorithm for efficient and high-precision detection of surface defects in UTFB was proposed in this study. Firstly, surface defect images of UTFB were collected from an actual production line and augmented to construct a comprehensive dataset. Then, using YOLOv8x as the baseline model, EfficientNet-ViT (EfficientNet-Vision Transformer) was introduced as the backbone network to achieve efficient feature extraction and improve the accuracy of object detection through improved self-attention mechanism and efficient Transformer architecture. Furthermore, the CIB (Compact Inverted Block) structure was utilized to optimize the C2f module, improving computational efficiency through efficient convolutional operations. Lastly, the WIoU loss function was introduced, with its dynamic focusing mechanism and improved gradient allocation strategy contributing to the detection of small-scale and multi-scale defects. Experimental results show that compared to the baseline YOLOv8x model, the improved YOLOv8x-ECW (YOLOv8x-EfficientNet-ViT-C2fCIB-WIoU) model achieved a 4.4% increase inmAP@0.5. The number of model parameters and floating-point operations (GFLOPS) were reduced by 61.1% and 62.7%, respectively, with a detection frame rate of 48.3 frames per second. The proposed YOLOv8x-ECW model can achieve efficient and accurate detection of surface defects in UTFB, providing technical support for online quality inspection of related wood products.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145170527","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

Feasibility Study of Radiographic Rail Inspections Using a Compact X-ray Tube 使用小型x射线管进行铁路射线照相检查的可行性研究

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-25 DOI: 10.1007/s10921-025-01193-x

Z. Chmieliński, J. Klimaszewski, T. Kosiński, D. Kowalczyk, M. Matusiak, J. Rzadkiewicz, S. Wronka, T. Zakrzewski

引用次数: 0

Practical Pose Estimation Method for Industrial X-ray Radiography Based on Deep Neural Network and Local Template Matching 基于深度神经网络和局部模板匹配的工业x射线成像实用位姿估计方法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-15 DOI: 10.1007/s10921-025-01191-z

Dongsheng Ou, Yongshun Xiao

{"title":"Practical Pose Estimation Method for Industrial X-ray Radiography Based on Deep Neural Network and Local Template Matching","authors":"Dongsheng Ou, Yongshun Xiao","doi":"10.1007/s10921-025-01191-z","DOIUrl":"10.1007/s10921-025-01191-z","url":null,"abstract":"<div><p>With the increasing integration of industrial products, critical components are increasingly being encapsulated within sealed enclosures, making it difficult to measure their actual positions during assembly using contact measurement techniques, often leading to substandard product quality. X-ray imaging provides a non-destructive solution for inspecting internal structures and accurately positioning internal components. However, traditional pose estimation methods based on X-ray imaging rely on projection optimization, which is time-consuming and cannot meet the timely feedback requirements of assembly processes. In this study, we propose a hybrid pose estimation method for industrial X-ray radiography that combines neural networks for initial pose estimation with local template matching for pose refinement. This approach achieves both high accuracy and efficiency in positioning internal targets. We conducted real X-ray imaging experiments on several objects, including a terahertz anode tube model. The mean alignment error was approximately 0.2 mm, lower than the spatial resolution (about 0.25 mm) of the CT images constructed from the same X-ray projections. The computation time for pose estimation of a single object was about 10 s, significantly faster than conventional methods that typically requiring several minutes, making it suitable for timely feedback in industrial assembly processes.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074004","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

Continuation Approach Combined with Semi-Analytical Finite-Element Method for Solving Guided-Wave Dispersion Equation 结合半解析有限元法求解导波色散方程的延拓法

IF 2.6 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-05-10 DOI: 10.1007/s10921-025-01194-w

Taizo Maruyama, Kazuyuki Nakahata

引用次数: 0