Journal Description
NDT
NDT
is an international, peer-reviewed, open access journal on non-destructive testing published quarterly online by MDPI. The Faringdon Research Centre for Non-Destructive Testing and Remote Sensing (FCNDT&RS) is affiliated with NDT and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- Rapid Publication: first decisions in 16 days; acceptance to publication in 5.8 days (median values for MDPI journals in the first half of 2024).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Latest Articles
Non-Destructive Testing of Concrete Materials from Piers: Evaluating Durability Through a Case Study
NDT 2024, 2(4), 532-548; https://doi.org/10.3390/ndt2040033 - 6 Dec 2024
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Concrete is currently the most used construction material, mainly due to its mechanical strength, chemical stability, and low cost. This material is affected by wear processes caused by the environment, which lead to a reduction in the useful life of the infrastructure in
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Concrete is currently the most used construction material, mainly due to its mechanical strength, chemical stability, and low cost. This material is affected by wear processes caused by the environment, which lead to a reduction in the useful life of the infrastructure in the long term. These wear processes can cause cracks, corrosion of reinforcing steel, loss of load capacity, and loss of concrete section, among other problems. Considering the above, it is necessary to carry out durability studies on concrete to determine the integrity conditions in which the infrastructure is found, the reasons for its deterioration, the environmental factors that affect it, and its useful life under these conditions, and develop restoration or protection plans. Generally, the durability studies include non-destructive testing such as ultrasonic pulse velocity, electrical resistivity, porosity measurement, and capillary absorption rate. These techniques make it possible to characterize the concrete and obtain information such as the total volume of pores, susceptibility to corrosion of the reinforcing steel, decrease in mechanical resistance, cracks, presence of humidity, and aggressive ions inside the concrete. In this work, two durability studies are presented with non-destructive tests carried out on active piers that are 20 and 40 years old. These are located in coastal areas in southern Mexico on the Gulf of Mexico side, with 80% average annual relative humidity, temperatures above 33 °C on average, high concentrations of salts, load handling, vibrations, flora and fauna typical of the marine ecosystem, etc. The results obtained reveal important information about the current state of the piers and the damage caused by the environment over time. This information allowed us to make decisions on preventive actions and develop appropriate and specific restoration projects for each pier.
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Open AccessArticle
Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning
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Leith Bounenni, Mohamed Arbane, Clemente Ibarra-Castanedo, Yacine Yaddaden, Sreedhar Unnikrishnakurup, Andrew Ngo Chun Yong and Xavier Maldague
NDT 2024, 2(4), 519-531; https://doi.org/10.3390/ndt2040032 - 2 Dec 2024
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Detecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared imaging techniques
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Detecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared imaging techniques to detect defects with high precision. The core contribution of our work lies in accurately detecting the size and depth of defects. Our method involves segmenting the size of the defect and calculating its centre to determine its depth. We achieve a more comprehensive and precise assessment of defects by integrating deep learning with infrared imaging based on the U-net model for segmentation and the CNN model for classification. The proposed model was rigorously tested on both a simulation dataset and an experimental dataset, demonstrating its robustness and effectiveness in accurately identifying and assessing defects on aerospace surfaces. The results indicate significant improvements in detection accuracy and computational efficiency, showing advancements over state-of-the-art methods and paving the way for enhanced maintenance protocols in the aerospace industry.
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Open AccessTechnical Note
Investigating Defect Detection in Advanced Ceramic Additive Manufacturing Using Active Thermography
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Anthonin Demarbaix, Enrique Juste, Tim Verlaine, Ilario Strazzeri, Julien Quinten and Arnaud Notebaert
NDT 2024, 2(4), 504-518; https://doi.org/10.3390/ndt2040031 - 15 Nov 2024
Abstract
Additive manufacturing of advanced materials has become widespread, encompassing a range of materials including thermoplastics, metals, and ceramics. For the ceramics, the complete production process typically involves indirect additive manufacturing, where the green ceramic part undergoes debinding and sintering to achieve its final
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Additive manufacturing of advanced materials has become widespread, encompassing a range of materials including thermoplastics, metals, and ceramics. For the ceramics, the complete production process typically involves indirect additive manufacturing, where the green ceramic part undergoes debinding and sintering to achieve its final mechanical and thermal properties. To avoid unnecessary energy-intensive steps, it is crucial to assess the internal integrity of the ceramic in its green stage. This study aims to investigate the use of active thermography for defect detection. The approach is to examine detectability using two benchmarks: the first focuses on the detectability threshold, and the second on typical defects encountered in 3D printing. For the first benchmark, reflection and transmission modes are tested with and without a camera angle to minimize reflection. The second benchmark will then be assessed using the most effective configurations identified. All defects larger than 1.2 mm were detectable across the benchmarks. The method can successfully detect defects, with transmission mode being more suitable since it does not require a camera angle adjustment to avoid reflections. However, the method struggles to detect typical 3D-printing defects because the minimum defect size is 0.6 mm, which is the size of the nozzle.
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(This article belongs to the Topic Nondestructive Testing and Evaluation)
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Open AccessArticle
Non-Destructive Estimation of Paper Fiber Using Macro Images: A Comparative Evaluation of Network Architectures and Patch Sizes for Patch-Based Classification
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Naoki Kamiya, Kosuke Ashino, Yasuhiro Sakai, Yexin Zhou, Yoichi Ohyanagi and Koji Shibazaki
NDT 2024, 2(4), 487-503; https://doi.org/10.3390/ndt2040030 - 7 Nov 2024
Abstract
Over the years, research in the field of cultural heritage preservation and document analysis has exponentially grown. In this study, we propose an advanced approach for non-destructive estimation of paper fibers using macro images. Expanding on studies that implemented EfficientNet-B0, we explore the
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Over the years, research in the field of cultural heritage preservation and document analysis has exponentially grown. In this study, we propose an advanced approach for non-destructive estimation of paper fibers using macro images. Expanding on studies that implemented EfficientNet-B0, we explore the effectiveness of six other deep learning networks, including DenseNet-201, DarkNet-53, Inception-v3, Xception, Inception-ResNet-v2, and NASNet-Large, in conjunction with enlarged patch sizes. We experimentally classified three types of paper fibers, namely, kozo, mitsumata, and gampi. During the experiments, patch sizes of 500, 750, and 1000 pixels were evaluated and their impact on classification accuracy was analyzed. The experiments demonstrated that Inception-ResNet-v2 with 1000-pixel patches achieved the highest patch classification accuracy of 82.7%, whereas Xception with 750-pixel patches exhibited the best macro-image-based fiber estimation performance at 84.9%. Additionally, we assessed the efficacy of the method for images containing text, observing consistent improvements in the case of larger patch sizes. However, limitations exist in background patch availability for text-heavy images. This comprehensive evaluation of network architectures and patch sizes can significantly advance the field of non-destructive paper analysis, offering valuable insights into future developments in historical document examination and conservation science.
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(This article belongs to the Special Issue Advances in Imaging-Based NDT Methods)
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Open AccessArticle
Evaluation of a Comprehensive Approach for the Development of the Field E* Master Curve Using NDT Data
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Konstantina Georgouli, Christina Plati and Andreas Loizos
NDT 2024, 2(4), 474-486; https://doi.org/10.3390/ndt2040029 - 24 Oct 2024
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Non-destructive testing (NDT) systems are essential tools and are widely used for assessing the condition and structural integrity of pavement structures without causing any damage. They are cost-effective, provide comprehensive data, and are time efficient. The bearing capacity and structural condition of a
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Non-destructive testing (NDT) systems are essential tools and are widely used for assessing the condition and structural integrity of pavement structures without causing any damage. They are cost-effective, provide comprehensive data, and are time efficient. The bearing capacity and structural condition of a flexible pavement depends on several interrelated factors, with asphalt layers stiffness being dominant. Since asphalt mix is a viscoelastic material, its performance can be fully captured by the dynamic modulus master curve. However, in terms of evaluating an in-service pavement, although a dynamic load is applied and the time history of deflections is recorded during testing of FWD, only the peak deflection is considered in the analysis. Therefore, the modulus of stiffness estimated by backcalculation is the modulus of elasticity. While several methods have been introduced for the determination of the field dynamic modulus master curve, the MEPDG approach provides significant advantages in terms of transparency and robustness. This study focuses on evaluating the methodology’s accuracy through an experimental study. The data analysis and validation process showed that routine measurements with the FWD and GPR, within the framework of a pavement monitoring system, can provide valuable input parameters for the evaluation of in-service pavements.
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Open AccessArticle
Description and Classification of Tempering Materials Present in Pottery Using Digital X-Radiography
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Alan Nagaya, Oscar G. de Lucio, Soledad Ortiz Ruiz, Eunice Uc González, Carlos Peraza Lope and Wilberth Cruz Alvarado
NDT 2024, 2(4), 456-473; https://doi.org/10.3390/ndt2040028 - 16 Oct 2024
Cited by 1
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Archaeological pottery X-radiography is mainly used for two applications: fabric characterization and identification of forming techniques. Both applications require imaging of tempering materials and other additives. With digital X-radiography, it is easy to enhance the image to compute and characterize these materials. In
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Archaeological pottery X-radiography is mainly used for two applications: fabric characterization and identification of forming techniques. Both applications require imaging of tempering materials and other additives. With digital X-radiography, it is easy to enhance the image to compute and characterize these materials. In this study, a combination of ImageJ plug-ins such as “threshold”, “analyze particles”, and “fit polynomial” were used to describe tempering materials of a set composed of archaeological pottery sherds. It was found that two different types of tempering materials were used. The first type was characterized by a grain size of less than 0.5 mm and no well-formed particles. In contrast, the second group had a grain size larger than 0.5 mm and well-formed particles.
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Open AccessArticle
Novel Statistical Analysis Schemes for Frequency-Modulated Thermal Wave Imaging for Inspection of Ship Hull Materials
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Ishant Singh, Vanita Arora, Prabhu Babu and Ravibabu Mulaveesala
NDT 2024, 2(4), 445-455; https://doi.org/10.3390/ndt2040027 - 15 Oct 2024
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In the field of thermal non-destructive testing and evaluation (TNDT&E), active thermography gained popularity due to its fast wide-area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression-favorable frequency-modulated thermal wave
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In the field of thermal non-destructive testing and evaluation (TNDT&E), active thermography gained popularity due to its fast wide-area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression-favorable frequency-modulated thermal wave imaging stands out for its enhanced detectability and depth resolution. In this study, an experimental investigation has been carried out on a hardened steel sample used in the ship building industry with a flat-bottom-hole-simulated defect using the frequency-modulated thermal wave imaging (FMTWI) technique. The defect detection capabilities of FMTWI have been investigated from various statistical post-processing approaches and compared by taking the signal-to-noise ratio (SNR) as a figure of merit. Among various adopted statistical post-processing techniques, pulse compression has been carried out using different methods, namely the offset removal with polynomial curve fitting and principal component analysis (PCA), which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. The performance of these techniques was assessed through experimental investigations on hardened steel specimens used in ship building to provide valuable insights into their effectiveness in defect detection capabilities.
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(This article belongs to the Special Issue Advances in Imaging-Based NDT Methods)
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Open AccessArticle
Integration of Non-Destructive Testing Technologies for Effective Monitoring and Evaluation of Road Pavements
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Christina Plati, Angeliki Armeni and Andreas Loizos
NDT 2024, 2(4), 430-444; https://doi.org/10.3390/ndt2040026 - 12 Oct 2024
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The successful management of road pavement maintenance requires the existence of suitable monitoring procedures for assessing pavement condition. A powerful tool for this is the use of non-destructive testing technologies. Non-destructive testing (NDT) aims to support the monitoring of pavement condition, as it
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The successful management of road pavement maintenance requires the existence of suitable monitoring procedures for assessing pavement condition. A powerful tool for this is the use of non-destructive testing technologies. Non-destructive testing (NDT) aims to support the monitoring of pavement condition, as it enables constant and rapid collection of in situ data. Analyzing NDT data can result in the development of useful indexes that can be related to trigger values (criteria) to define pavement condition. This information can be used to assess the “health” of the pavement to decide whether intervention is required. However, to effectively support the implementation of pavement management measures, it is sometimes necessary to implement a pavement monitoring and assessment framework that can be adapted by road authorities on a case-by-case basis. To this end, this study addresses the development of a pavement monitoring and assessment procedure by integrating different NDT technologies to collect and evaluate data. The procedure, referred to as Integrated Testing and Evaluation (ITE), is proposed as an algorithm to find optimal strategies for prioritizing potential pavement interventions, considering the budget constraints for the required investigations.
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Open AccessArticle
Skeletal Muscle Oxidative Metabolism during Exercise Measured with Near Infrared Spectroscopy
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Kevin K. McCully, Sarah N. Stoddard, Mary Ann Reynolds and Terence E. Ryan
NDT 2024, 2(4), 417-429; https://doi.org/10.3390/ndt2040025 - 11 Oct 2024
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This study characterized the level of oxidative metabolism in skeletal muscle during whole-body activity as a percentage of the muscle’s maximum oxidative rate (mVO2max) using near-infrared spectroscopy (NIRS). Ten healthy participants completed a progressive work test and whole-body walking and lunge
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This study characterized the level of oxidative metabolism in skeletal muscle during whole-body activity as a percentage of the muscle’s maximum oxidative rate (mVO2max) using near-infrared spectroscopy (NIRS). Ten healthy participants completed a progressive work test and whole-body walking and lunge exercises, while oxygen saturation was collected from the vastus lateralis muscle using near-infrared spectroscopy (NIRS). Muscle oxygen consumption (mVO2) was determined using arterial occlusions following each exercise. mVO2max was extrapolated from the mVO2 values determined from the progressive exercise test. mVO2max was 11.3 ± 3.3%/s on day one and 12.0 ± 2.9%/s on day two (p = 0.07). mVO2max had similar variation (ICC = 0.95, CV = 6.4%) to NIRS measures of oxidative metabolism. There was a progressive increase in mVO2 with walking at 3.2 Km/h, 4.8 km/h, 6.4 Km/h, and with lunges (15.8 ± 6.6%, 20.5 ± 7.2%, 26.0 ± 6.6%, and 57.4 ± 15.4% of mVO2max, respectively). Lunges showed a high reliability (ICC = 0.81, CV = 10.2%). Muscle oxidative metabolism in response to whole-body exercise can be reproducibly measured with arterial occlusions and NIRS. This method may be used to further research on mitochondrial activation within a single muscle during whole-body exercise.
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Open AccessReview
Advances in Spectroscopic Methods for Predicting Cheddar Cheese Maturity: A Review of FT-IR, NIR, and NMR Techniques
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Sanja Seratlic, Bikash Guha and Sean Moore
NDT 2024, 2(4), 392-416; https://doi.org/10.3390/ndt2040024 - 6 Oct 2024
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The quest for reliable techniques to predict Cheddar cheese maturity has gained momentum to ensure quality and consistency in large-scale production. Given the complexity of cheese ripening and the industry’s need for fast and reliable evaluation methods, this review addresses the challenge by
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The quest for reliable techniques to predict Cheddar cheese maturity has gained momentum to ensure quality and consistency in large-scale production. Given the complexity of cheese ripening and the industry’s need for fast and reliable evaluation methods, this review addresses the challenge by scrutinising the application of spectroscopic techniques such as Fourier transform infrared (FT-IR), near-infrared (NIR), and nuclear magnetic resonance (NMR). These methods are evaluated for their noninvasive and rapid on-site analysis capabilities, which are essential for ensuring quality in cheese production. This review synthesises current research findings, discusses the potential and limitations of each technique, and highlights future research directions. Overall, NIR spectroscopy emerges as the most promising, offering quick, nondestructive assessments and reasonably accurate compositional predictions, crucial for real-time maturation monitoring. It provides rapid results within minutes, making it significantly faster than FT-IR and NMR. While FT-IR also offers high accuracy, it typically requires longer analysis times due to extensive calibration and can be sensitive to sample conditions, while NMR, although highly accurate, involves complex and time-consuming procedures. Nonetheless, further studies are necessary to refine these spectroscopic techniques, enhance their predictive accuracy, and deepen the understanding of the correlations between chemical attributes and sensory qualities in Cheddar cheese.
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Open AccessArticle
Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography
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Bata Hena, Gabriel Ramos, Clemente Ibarra-Castanedo and Xavier Maldague
NDT 2024, 2(4), 378-391; https://doi.org/10.3390/ndt2040023 - 4 Oct 2024
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Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation solutions in manufacturing, serving safety-critical sectors
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Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation solutions in manufacturing, serving safety-critical sectors such as automotive and aerospace industries. However, this method of component fabrication is very susceptible to generating manufacturing flaws, hence necessitating adequate non-destructive testing (NDT) to ascertain the fitness for use of such components. Machine learning has taken the center stage in recent years as a tool for developing automated solutions for detecting and classifying flaws in digital X-ray radiography. These machine learning-based solutions have increasingly been developed and deployed for component inspection, to keep pace with the high production throughput in manufacturing industries. This work focuses on the development of a defect grading algorithm that assesses detected flaws to ascertain if they constitute a defect that could render a component unfit for use. Guided by ASTM 2973-15; Standard Digital Reference Images for Inspection of Aluminum and Magnesium Die Castings, a grading pipeline utilizing K-D (k-dimensional) trees was developed to effectively structure detected flaws, enabling the system to make decisions based on acceptable grading terms. This solution is dynamic in terms of its conformity to different grading criteria and offers the possibility to achieve automated decision making (Accept/Reject) in digital X-ray radiography applications.
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Open AccessArticle
Imaging and Image Fusion Using GPR and Ultrasonic Array Data to Support Structural Evaluations: A Case Study of a Prestressed Concrete Bridge
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Thomas Schumacher
NDT 2024, 2(3), 363-377; https://doi.org/10.3390/ndt2030022 - 13 Sep 2024
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To optimally preserve and manage our civil structures, we need to have accurate information about their (1) geometry and dimensions, (2) boundary conditions, (3) material properties, and (4) structural conditions. The objective of this article is to show how imaging and image fusion
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To optimally preserve and manage our civil structures, we need to have accurate information about their (1) geometry and dimensions, (2) boundary conditions, (3) material properties, and (4) structural conditions. The objective of this article is to show how imaging and image fusion using non-destructive testing (NDT) measurements can support structural engineers in performing accurate structural evaluations. The proposed methodology involves imaging using synthetic aperture focusing technique (SAFT)-based image reconstruction from ground penetrating radar (GPR) as well as ultrasonic echo array (UEA) measurements taken on multiple surfaces of a structural member. The created images can be combined using image fusion to produce a digital cross-section of the member. The feasibility of this approach is demonstrated using a case study of a prestressed concrete bridge that required a bridge load rating (BLR) but where no as-built plans were available. Imaging and image fusion enabled the creation of a detailed cross-section, allowing for confirmation of the number and location of prestressing strands and the location and size of internal voids. This information allowed the structural engineer of record (SER) to perform a traditional bridge load rating (BLR), ultimately avoiding load restrictions being imposed on the bridge. The proposed methodology not only provides useful information for structural evaluations, but also represents a basis upon which the digitalization of our infrastructure can be achieved.
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Open AccessArticle
Microwave Imaging and Non-Destructive Testing of Bituminous Mix Binder-Aggregate Behavior Using Log-Periodic Feedline-Based Microstrip Filter
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Amartya Paul, Hemant Kumari, Rinaldo Snaitang, Pradeep Kumar Gautam and Shubhankar Majumdar
NDT 2024, 2(3), 347-362; https://doi.org/10.3390/ndt2030021 - 29 Aug 2024
Abstract
This research investigates the characterization of bituminous mixes utilizing microwave imaging and non-destructive testing. We studied the electromagnetic characteristics of various samples, including bituminous concrete (BC) and open-grade friction course (OGFC) samples. A novel ring filter with log-periodic feedlines, designed on the RT/Duroid
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This research investigates the characterization of bituminous mixes utilizing microwave imaging and non-destructive testing. We studied the electromagnetic characteristics of various samples, including bituminous concrete (BC) and open-grade friction course (OGFC) samples. A novel ring filter with log-periodic feedlines, designed on the RT/Duroid 5880 substrate, was utilized within the frequency range of 0.3–0.7 GHz. The samples were assessed using average attenuation and group delay measures, which detailed clear electromagnetic characteristics. The samples’ flow value and specific gravity were correlated to these parameters. The calculated flow value and specific gravity (using the filter) and measured flow value and specific gravity (using the conventional method) coincided well. The filter could predict the parameters of the samples with a high accuracy of roughly 99.8% for the flow value and specific gravity, whereas the OGFC sample displayed an accuracy of 99.7%, correspondingly, as shown in high R2 values. This demonstrates that the filter can precisely measure the parameters required for studying the interaction between the binder and aggregate in bituminous mixes without being invasive. The findings indicate a significant disparity between OGFC and BC samples in their responses to electromagnetic fields and their characteristics. This demonstrates the high sensitivity and significant value of microwave techniques in the study of bitumen and the construction of roadways.
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(This article belongs to the Topic Nondestructive Testing and Evaluation)
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Open AccessTechnical Note
Modular, Physically Motivated Simulation Model of an Ultrasonic Testing System
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Marius W. Schäfer and Sarah C. L. Fischer
NDT 2024, 2(3), 330-346; https://doi.org/10.3390/ndt2030020 - 29 Aug 2024
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The increasing complexity of material systems requires an extension of conventional non-destructive evaluation methods such as ultrasonic testing. Many publications have worked on extending simulation models to cover novel aspects of ultrasonic transducers, but they do not cover all components of the system.
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The increasing complexity of material systems requires an extension of conventional non-destructive evaluation methods such as ultrasonic testing. Many publications have worked on extending simulation models to cover novel aspects of ultrasonic transducers, but they do not cover all components of the system. This paper presents a physically motivated, modular model that describes the complete signal flow with the aim of providing a platform for optimizing ultrasonic testing systems from individual components to the whole system level. For this purpose, the ultrasonic testing system is divided into modules, which are described by models. The modules are each parameterized by physical parameters, characteristics of real components as provided by datasheets, or by measurements. In order to validate the model, its performance is presented for three different configurations of a real test system, considering both classical sinusoidal excitation and a chirp signal. The paper demonstrates the modularity of the model, which can be adapted to the different configurations by simply adapting the modified component, thus drastically reducing the complexity of modeling a complex ultrasonic system compared to State-of-the-Art models. Based on this work, ultrasonic inspection systems can be optimized for complex applications, such as operation with coded excitation, which is a major challenge for the system components.
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Open AccessArticle
A Methodology to Manage and Correlate Results of Non-Destructive and Destructive Tests on Ancient Timber Beams: The Case of Montorio Tower
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Anna Marzo, Bruno Carpani, Giuseppe Marghella and Concetta Tripepi
NDT 2024, 2(3), 311-329; https://doi.org/10.3390/ndt2030019 - 5 Aug 2024
Cited by 1
Abstract
Intending to safeguard architectural heritage, the assessment of the health of timber structures is crucial, though challenging, due to the organic nature of wood and to the uncertainties of its preservation state. To this end, useful support is provided by the ICOMOS guidelines,
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Intending to safeguard architectural heritage, the assessment of the health of timber structures is crucial, though challenging, due to the organic nature of wood and to the uncertainties of its preservation state. To this end, useful support is provided by the ICOMOS guidelines, which provide conservation strategies based on thorough diagnosis and safety evaluations. In this context, the study summarized in this paper focuses on the medieval Tower of Montorio, which suffered considerable damage due to the strong earthquake that occurred in those area in September 2003. Its subsequent process of rehabilitation and restoration involved a widespread experimental campaign and the substitution of some timber beams. This circumstance has offered a rare opportunity to study these ancient elements in detail, although they are limited in number. Six beams made of oak and removed from an intermediate floor of the tower were evaluated through a comprehensive approach that included both non-destructive tests (NDT) and destructive tests (DT). Particularly, they were subjected to visual inspections, ultrasonic, sclerometric, and resistographic methods, and destructive four-point bending tests. Overall, the study presented here provides a useful qualitative comparison between them. Results highlighted that relying only on NDT might lead to an overestimation of mechanical properties and that combining NDT with DT is crucial for a more accurate assessment. Therefore, the need to deepen the research on correlations between NDT and DT to obtain reliable values of mechanical properties while respecting the conservation aim was confirmed.
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(This article belongs to the Topic Nondestructive Testing and Evaluation)
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Open AccessSystematic Review
Reviewing Material-Sensitive Computed Tomography: From Handcrafted Algorithms to Modern Deep Learning
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Moritz Weiss and Tobias Meisen
NDT 2024, 2(3), 286-310; https://doi.org/10.3390/ndt2030018 - 30 Jul 2024
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Computed tomography (CT) is a widely utilised imaging technique in both clinical and industrial applications. CT scan results, presented as a volume revealing linear attenuation coefficients, are intricately influenced by scan parameters and the sample’s geometry and material composition. Accurately mapping these coefficients
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Computed tomography (CT) is a widely utilised imaging technique in both clinical and industrial applications. CT scan results, presented as a volume revealing linear attenuation coefficients, are intricately influenced by scan parameters and the sample’s geometry and material composition. Accurately mapping these coefficients to specific materials is a complex task. Traditionally, material decomposition in CT relied on classical algorithms using handcrafted features based on X-ray physics. However, there is a rising trend towards data-driven approaches, particularly deep learning, which offer promising improvements in accuracy and efficiency. This survey explores the transition from classical to data-driven approaches in material-sensitive CT, examining a comprehensive corpus of literature identified through a detailed and reproducible search using Scopus. Our analysis addresses several key research questions: the origin and generation of training datasets, the models and architectures employed, the extent to which deep learning methods reduce the need for domain-specific expertise, and the hardware requirements for training these models. We explore the implications of these findings on the integration of deep learning into CT practices and the potential reduction in the necessity for extensive domain knowledge. In conclusion, this survey highlights a significant shift towards deep learning in material-resolving CT and discusses the challenges and opportunities this presents. The transition suggests a future where data-driven approaches may dominate, offering enhanced precision and robustness in material-resolving CT while potentially transforming the role of domain experts in the field.
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Open AccessArticle
Towards Non-Destructive Quality Testing of Complex Biomedical Devices—A Generalized Closed-Loop System Approach Utilizing Real-Time In-Line Process Analytical Technology
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Bikash Guha, Sean Moore and Jacques Huyghe
NDT 2024, 2(3), 270-285; https://doi.org/10.3390/ndt2030017 - 26 Jul 2024
Cited by 1
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This study addresses the critical issue of cardiovascular diseases (CVDs) as the leading cause of death globally, emphasizing the importance of stent delivery catheter manufacturing. Traditional manufacturing processes, reliant on destructive end-of-batch sampling, present significant financial and quality challenges. This research addresses this
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This study addresses the critical issue of cardiovascular diseases (CVDs) as the leading cause of death globally, emphasizing the importance of stent delivery catheter manufacturing. Traditional manufacturing processes, reliant on destructive end-of-batch sampling, present significant financial and quality challenges. This research addresses this challenge by proposing a novel approach: a closed-loop cyber-physical production system (CPPS) employing non-destructive process analytical technology (PAT). Through a mixed-method approach combining a comprehensive literature review and the development of a CPPS prototype, the study demonstrates the potential for real-time quality control, reduced production costs, and increased manufacturing efficiency. Initial findings showcase the system’s effectiveness in streamlining production, enhancing stability, and minimizing defects, translating to substantial financial savings and improved product quality. This work extends the author’s previous research by comparing the validated system’s performance to that of pre-implementation manual workflows and inspections, highlighting tangible and intangible improvements brought by the new system. This paves the way for advanced control strategies to revolutionize medical device manufacturing. Furthermore, the study proposes a generalized CPPS framework applicable across diverse regulated environments, ensuring optimal processing conditions and adherence to stringent regulatory standards. The research concludes with the successful demonstration of innovative approaches and technologies, leading to improved product quality, patient safety, and operational efficiency in the medical device industry.
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Open AccessArticle
Monitoring of Wall Thickness to Predict Corrosion in Marine Environments Using Ultrasonic Transducers
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Francisca Salgueiro, Mário Ribeiro, André Carvalho, Guilherme Covas, Øystein Baltzersen and Carla Sofia Proença
NDT 2024, 2(3), 255-269; https://doi.org/10.3390/ndt2030016 - 26 Jul 2024
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The research related to subsea inspection, and the prediction of corrosion is a challenging task, and the progress in this area is continuously generating exciting new developments that may be used in subsea inspection. Wall thickness monitoring is an important tool to control
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The research related to subsea inspection, and the prediction of corrosion is a challenging task, and the progress in this area is continuously generating exciting new developments that may be used in subsea inspection. Wall thickness monitoring is an important tool to control and predict corrosion, such as on platforms for the infrastructure of floating offshore wind power production. This study shows the results obtained in marine environments. For this experiment, a steel plate equipped with ultrasound transducers was placed in seawater to corrode naturally. The sensor test setup consisted of 15 ultrasound transducers and 1 temperature sensor, which were installed in the cassette. The data acquisition system was based on a standard industrial computer with software written in Python and MATLAB. The ultrasound signals were collected at regular intervals and processed to calculate the instantaneous wall thickness. The progress of corrosion was evaluated by trend plots of wall thickness versus time, and the change in shape of the ultrasonic back wall reflection waveform measured by each sensor.
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Open AccessCommunication
The INFN-LNF Astrophysics and Cosmology Integrated Test Facility Startup
by
Luca Porcelli, Sultan Dabagov, Giovanni Delle Monache, Dariush Hampai, Giuseppina Modestino and Sandra Savaglio
NDT 2024, 2(3), 249-254; https://doi.org/10.3390/ndt2030015 - 12 Jul 2024
Abstract
Starting from January 2023, Permanent Staff Personnel and Associated Personnel of INFN-LNF (Istituto Nazionale di Fisica Nucleare—Laboratori Nazionali di Frascati) have founded, and are setting up, the local Astrophysics and Cosmology Team (ACT). The INFN-LNF ACT joined the initial development phases of one
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Starting from January 2023, Permanent Staff Personnel and Associated Personnel of INFN-LNF (Istituto Nazionale di Fisica Nucleare—Laboratori Nazionali di Frascati) have founded, and are setting up, the local Astrophysics and Cosmology Team (ACT). The INFN-LNF ACT joined the initial development phases of one of the forthcoming (early 2030) next-generation cosmology space-borne probes, with particular emphasis on (1) thermal balance tests (and correlation to models) of the electronics of interest; (2) (non)destructive irradiation tests of the electronics of interest and X-ray circuitry diagnostics on a specifically dedicated and instrumented optical bench; and (3) joining the simulation-related, and data analysis-related, activities, at both the cosmological and instrumental levels. The INFN-LNF ACT has constituted an Integrated Test Facility (ITF), which is being instrumented in a dedicated space and will also make use of the pre-existing INFN-LNF infrastructures. In the following, as a first contribution, mainly related to what was completed in late 2023 and early 2024, the activities of the commissioning and setup of the so-called ‘pocket’ cryostat are described, linking them to the envisaged thermal balance tests (and correlation to the models) of the electronics of interest. While mainly devoted to cosmology-oriented tasks, the INFN-LNF ACT ‘pocket’ cryostat will, in principle, be available to the wider community for other dedicated activities.
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(This article belongs to the Topic Nondestructive Testing and Evaluation)
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Open AccessArticle
Non-Destructive Inspection of Additively Manufactured Classified Components in a Nuclear Installation
by
Alfredo Lamberti, Wouter Van Eesbeeck and Steve Nardone
NDT 2024, 2(3), 228-248; https://doi.org/10.3390/ndt2030014 - 11 Jul 2024
Abstract
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Additive Manufacturing (AM) of parts used in nuclear power plants can solve many issues like those related to obsolescence. Of the gap limiting the use of AM parts in nuclear is the need of reliable non-destructive inspection capable to meet the qualification requirements.
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Additive Manufacturing (AM) of parts used in nuclear power plants can solve many issues like those related to obsolescence. Of the gap limiting the use of AM parts in nuclear is the need of reliable non-destructive inspection capable to meet the qualification requirements. Recently, efforts in this direction have been made worldwide within several research projects, like the EU Horizon 2020 NUCOBAM. In the framework of NUCOBAM, this article presents the activity related to the inspection of 316-L AM nuclear parts produced by L-PBF and inspected via advanced ultrasonic (UT) methods, like MultiPoint Focusing (MPF) and Total Focusing Method (TFM). Multiple UT array probes are used, linear, matrix and annular. Emphasis is dedicated to the inspection of classified valve bodies produced with known internal seeding flaws. The analysis of the results shows the effect of AM induced anisotropy on the propagation of the ultrasonic wave characteristics, the sound velocity increased with 3% when the sound beam deviated 15° against the perpendicular axis. The TFM method contributed significantly regarding defect detection, Signal to Noise Ratios (SNR) increased with at least 9 dB compared to the Multi-Point Focusing method. Smaller errors were noticed when examination frequency was increased and TFM was applied. The combination of an annular array with TFM and mechanical scanning demonstrated to be the best approach.
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