Journal of Nondestructive Evaluation最新文献

{"title":"Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning","authors":"Herminso Villarraga-Gómez,&nbsp;Paul Brackman,&nbsp;Amirkoushyar Ziabari,&nbsp;Obaidullah Rahman,&nbsp;Zackary Snow,&nbsp;Ravi Shahani,&nbsp;Katrin Bugelnig,&nbsp;Andriy Andreyev,&nbsp;Yulia Trenikhina,&nbsp;Nathan Johnson,&nbsp;Hrishikesh Bale,&nbsp;Julian Schulz,&nbsp;Edson Costa Santos","doi":"10.1007/s10921-025-01231-8","DOIUrl":"10.1007/s10921-025-01231-8","url":null,"abstract":"<div>\u0000 \u0000 <p>Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.</p>\u0000 </div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01231-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: MLP ANN Equipped Approach To Measuring Scale Layer in Oil-Gas-Water Homogeneous Fluid by Capacitive and Photon Attenuation Sensors","authors":"Abdulilah Mohammad Mayet,&nbsp;Salman Arafath Mohammed,&nbsp;Evgeniya Ilyinichna Gorelkina,&nbsp;Robert Hanus,&nbsp;John William Grimaldo Guerrero,&nbsp;Shamimul Qamar,&nbsp;Hassen Loukil,&nbsp;Neeraj Kumar Shukla,&nbsp;Rafał Chorzępa","doi":"10.1007/s10921-025-01237-2","DOIUrl":"10.1007/s10921-025-01237-2","url":null,"abstract":"","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861395","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}

{"title":"Exploring Image Quality Improvements in High-Speed Dual Threshold Photon-Counting Micro-CT","authors":"Till Dreier,&nbsp;Jenny Romell,&nbsp;Carlo Amato,&nbsp;Spyridon Gkoumas","doi":"10.1007/s10921-025-01250-5","DOIUrl":"10.1007/s10921-025-01250-5","url":null,"abstract":"<div><p>High-speed X-ray computed tomography (CT) of batteries in-line or at-line is a promising technique to obtain quality-relevant insights leading to an optimized production process and detection of defective batteries. By using a high-power micro-focus X-ray source and a photon-counting detector, CT scans can be obtained within seconds. Here we explore utilizing the simultaneous readout of multiple images at different energy-discriminating thresholds and recombining them to improve the quality of the reconstructed volumes to optimize different quality parameters relevant to battery inspection. Using a-priori knowledge, threshold optimization is performed. Evaluating the combined volumes shows that there is an ideal threshold, or combination of two thresholds, depending on what matric used to optimize contrast between specific feature of a specific sample. Further, the contrast of the jelly roll compared to the rest of the battery can also be improved by combining two different thresholds. The experiments highlight the importance of threshold optimization and the potential gain of combining two simultaneous acquisitions using different energy thresholds for fast CT scans with limited photon statistics.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01250-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Lightweight RT-DETR Model for Metal Surface Defect Detection Using Multi-Scale Network and Additive Attention Mechanism","authors":"Zongchen Hao,&nbsp;Bo Liu,&nbsp;Binrui Xu","doi":"10.1007/s10921-025-01251-4","DOIUrl":"10.1007/s10921-025-01251-4","url":null,"abstract":"<div><p>In the industrial production of metals, surface defect detection is crucial for ensuring product quality and optimizing production line efficiency. Although deep learning algorithms are effective for detecting metal surface defects, their complexity can often slow down the detection process. To achieve a balance between detection accuracy and efficiency, this study proposes an enhanced and lightweight Real-Time Detection Transformer (RT-DETR) network and incorporates a multi-scale residual feature extraction (MSRFE) module, termed as MSRFE-RTDETR. The MSRFE module is specifically designed to manage varying defect shapes while reducing the parameter count. To further enhance detection accuracy, a context feature information fusion (CFIF) module is introduced, which integrates deep and shallow features to prevent information loss. Additionally, an efficient encoder based on additive attention (EEAA) is employed to overcome the limitations of matrix multiplication inherent in traditional multi-head attention mechanisms, thereby increasing the model's detection speed. Compared to the baseline model, the proposed algorithm improves the average precision on the public NEU-DET dataset by 2.4%, increases detection speed by 39.69 FPS, and enhances all lightweight metrics. Its generalization is validated on GC10-DET and ASSDD datasets, demonstrating superior performance.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861576","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}

{"title":"Ultrasonic Imaging Technique for NDE: Arbitrary Virtual Array Source Aperture with using Sign Coherence Factor","authors":"Thulsiram Gantala,&nbsp;Krishnan Balasubramaniam","doi":"10.1007/s10921-025-01249-y","DOIUrl":"10.1007/s10921-025-01249-y","url":null,"abstract":"<div><p>In this paper, we propose the ultrasound imaging method, arbitrary virtual array sources aperture (AVASA), using signal sign coherence (SC) information to inspect thick, highly attenuating structural components and enhance image resolution. The AVASA-SC employs phased array (PA) parallel transmission to focus beamforming at multiple virtual sources, improve the signal-to-noise ratio (SNR) of received A-scan signals, and record the reflected signals with all the array elements. The high-resolution imaging is reconstructed on the reception by an AVASA beamformer that virtually focuses on each point in the inspection region through the coherence summing of the signal sign bit, reducing image processing time. AVASA effectively images thicker structures by focusing the ultrasound beam at virtual sources through exciting parallel transmission. However, in AVASA, the SNR of deeper reflectors can be reduced due to signal amplitude-based image reconstruction. Therefore, AVASA-SC uses the instantaneous signal sign bit of the AVASA beamforming aperture data to create imaging. To compare AVASA-SC’s defect SNR and imaging resolution for deeper-located defects, two test samples (one with known defects, one with unknown) were scanned using AVASA and full matrix capture-total focusing method (FMC-TFM) techniques. AVASA-SC significantly improves image resolutions, enabling enhanced defect characterization.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861577","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}

{"title":"A New Method for the Microfocus X-ray Computed Tomography Visualization and Quantitative Exploration of Reinforcement Particles in Additively Manufactured Superalloy IN718","authors":"I-Ting Ho,&nbsp;Devin Bayly,&nbsp;Pascal Thome,&nbsp;Sammy Tin","doi":"10.1007/s10921-025-01243-4","DOIUrl":"10.1007/s10921-025-01243-4","url":null,"abstract":"<div><p>This study presents a quantitative analysis of CeO₂ and TiB₂ non-metallic particles within the microstructure of additively manufactured (AM) Ni-superalloys Inconel 718 (IN718), using microfocus X-ray computed tomography (micro-XCT) and a volumetric analysis tool, CGAL VESPA Alpha Wrapping. Focusing on the characterization of CeO₂ and TiB₂ particles embedded within IN718, this method highlights their size and volume fraction variations as well as distinct spatial distributions, which are quantitatively compared to metallographically prepared SEM samples. Quantitative assessments conducted with Paraview served as the basis for optimizing alpha and offset parameters for surface construction. This optimized data processing routine yields volume and surface morphology estimations that more closely align with those obtained from SEM observations, compared to the traditional Marching Cubes algorithm, assuming identical preprocessing and binarization standards. The flexibility to adjust the wrapping parameters also allows for precise control over volumetric and surface area estimations. The results demonstrated that CGAL VESPA Alpha Wrapping, implemented in Paraview for object identification, enables simultaneous evaluation of particle morphology and authentic volumetric information from the same micro-XCT data, particularly for non-uniformly distributed reinforcement particles. This capability supports a more reliable non-destructive evaluation for AM components.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861559","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}

{"title":"Review of Current Trends and Uses of Machine Learning for Discrete Acoustic Emission Interpretation","authors":"Maël Pénicaud,&nbsp;Florence Lequien,&nbsp;Clément Fisher,&nbsp;Arnaud Recoquillay","doi":"10.1007/s10921-025-01247-0","DOIUrl":"10.1007/s10921-025-01247-0","url":null,"abstract":"<div><p>Acoustic Emission (AE) is a well-established and recognised technique for monitoring the degradation of a variety of structures. It is used in a variety of applications, including fatigue monitoring, corrosion monitoring, or detection of pressure leaks. As sensors evolve and databases grow, analysis allows for a better interpretation and understanding of phenomena. Specifically, the usage of Machine Learning (ML) algorithms has proven to be a major tool for interpreting signals. This paper reviews the current usage of ML algorithms used in major Acoustic Emission applications to interpret damage mechanisms, exploring how ML allows the study of more complex phenomena and structures, discussing the conditions, precautions and limitations to its usage as well as future prospects and potentials.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01247-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Limited-Sample Probability of Detection Estimation Using Models and Advanced Regression Techniques","authors":"Qizheng Xia,&nbsp;John C. Aldrin,&nbsp;Qing Li","doi":"10.1007/s10921-025-01232-7","DOIUrl":"10.1007/s10921-025-01232-7","url":null,"abstract":"<div><p>The probability of detection (POD) is a fundamental metric for evaluating the performance of nondestructive evaluation (NDE) techniques. However, traditional empirical approaches to POD estimation often require extensive measurements, making them costly in terms of time, budget, and resources. In scenarios with limited data, conventional estimation methods frequently fail to capture the underlying relationship between signal responses and flaw sizes, as well as the variability introduced by testing conditions, influencing factors, and inherent uncertainties. Moreover, standard linear regression models, commonly used in POD analysis, rely on assumptions that are often violated when sample sizes are small, resulting in biased or imprecise estimates. To overcome these challenges, this study investigates advanced regression techniques and their integration with physics-based models for limited-sample POD (LS-POD) estimation. LS-POD here is defined as POD estimation when the sample size is below the threshold typically required by conventional methods. We explore a range of information-augmentation approaches, including physics-informed regression and Bayesian methods, which incorporate prior knowledge to improve the characterization of the signal-flaw relationship and the variability of NDE procedures. Additionally, we adapt advanced statistical techniques, such as Box-Cox transformation, robust regression, weighted linear regression, and bootstrapping, to mitigate the impact of assumption violations commonly encountered in small-sample contexts. These methods are further integrated to simultaneously leverage existing knowledge and address statistical assumption violations. We conduct comprehensive simulation studies using both synthetic and empirical datasets to evaluate the performance of these approaches under a variety of LS-POD scenarios. The results are benchmarked against conventional POD estimates derived from large-sample data. Our findings indicate that incorporating prior knowledge and employing assumption-resilient regression techniques can significantly enhance the accuracy and precision of LS-POD estimation. The combined use of information-augmentation and assumption-correction strategies yields further improvements. These results provide practical insights for NDE practitioners, facilitating the selection and application of appropriate LS-POD methods tailored to specific data conditions and application needs.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861603","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}

{"title":"Quality Evaluation of Additive Manufacturing Components Based on Zero-Group-Velocity Lamb Waves","authors":"Meng Ren,&nbsp;Xiangdi Meng,&nbsp;Mingxi Deng","doi":"10.1007/s10921-025-01246-1","DOIUrl":"10.1007/s10921-025-01246-1","url":null,"abstract":"<div><p>In the production process of additive manufacturing (AM) components, the occurrence of holes, microcracks, and other defects can seriously affect the physical and mechanical properties of AM components. This paper presents an effective method for quality evaluation of AM components utilizing zero-group-velocity (ZGV) Lamb waves. The displacement distribution and propagation characteristics of the S1-ZGV mode in the AM component are analyzed in detail by the finite element (FE) method, and the changes in the S1-ZGV mode under different quality levels (characterized by different Young’s moduli) are investigated. The results indicate that the S1-ZGV mode in the AM component is distributed in the form of standing waves, whose time-domain waveform persists throughout the entire time-domain. As the level of quality deteriorates, a corresponding reduction is observed in both the frequency and spectral amplitude (SA) of the S1-ZGV mode, and notably, the SA at the initial S1-ZGV frequency (in good material condition) significantly decreases. This observation provides a reliable method for conducting effective quality evaluation of AM components. Subsequently, the S1-ZGV mode is experimentally and successfully excited in the AM component using the pitch-catch technique with air-coupled ultrasonic transducers, and the SA at different detected positions is quantitatively observed to validate the effectiveness of the method. The experimental results reveal that compared to the traditional linear ultrasonic technique based on wave velocity measurement, the SA at the initial S1-ZGV frequency can more effectively evaluate the quality level of the AM component, which are verified by the optical microscope images. These results validate the effectiveness of the SA based on ZGV modes in accurately evaluating the quality level of the AM components.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861604","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}

{"title":"3D Modeling of Ultrasonic Wave Propagation in Pervious Concrete","authors":"Agustin Spalvier,&nbsp;Juan Sánchez,&nbsp;Nicolás Pérez","doi":"10.1007/s10921-025-01248-z","DOIUrl":"10.1007/s10921-025-01248-z","url":null,"abstract":"<div><p>Ultrasonic testing is a widely employed non-destructive technique for material characterization and defect detection. For pervious concrete (PeC), a porous composite material made of cement paste and coarse aggregate, understanding the interaction between material properties and ultrasonic wave propagation remains a challenge. This study implements a three-dimensional finite element model to simulate acoustic wave behavior in PeC, focusing on the effects of porosity <i>P</i>, aggregate size <i>D</i>, elastic modulus <i>E</i>, and density <span>(rho )</span>. The specific goal is to understand the relationship of ultrasonic wave velocity and porosity in PeC. To control porosity, the model is based on a simplified hypothetical contact between particles which may represent the cement paste surrounding the aggregate particles. Several families of models are built by varying porosity between 8% and 40%, and three different values of <i>D</i>, <i>E</i> and <span>(rho )</span>. An analytical model –an equation– is proposed and successfully fitted to the numerical data, and then tested numerically; the equation consists of a theoretical P-wave velocity multiplied by a factor dependent of <i>D</i> and <i>P</i>. Numerical results are partially validated against experimental measurements obtained from PeC samples with porosity values ranging from 14% to 35%. The findings reveal a clear inverse relationship between porosity and ultrasonic wave velocity, emphasizing the influence of aggregate contact areas. This work establishes a foundation for advancing ultrasonic testing as a reliable tool for assessing PeC porosity and performance in field applications.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861591","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}