NDT

Nondestructive testing (NDT) has a crucial role in ensuring the reliability and safety of industrial systems. However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. The current study examined an alternative approach using variable-frequency drive (VFD) data for real-time fault detection and predictive maintenance. Most VFDs continuously monitor essential parameters such as motor speed, torque, efficiency, and power consumption, facilitating sensorless condition monitoring that helps detect early-stage motor and apparatus faults without additional hardware. To improve diagnostic capabilities, calculated metrics such as apparent power, efficiency, torque, and energy consumption can deliver more profound insights into system performance, assisting in identifying potential failure patterns. A Python-based data acquisition and visualization system was developed and implemented as an example of a potential solution, enabling centralized monitoring, anomaly detection, and historical data analysis. Future advancements in artificial intelligence and machine learning could further refine automated fault detection by utilizing historical VFD data to predict system failures accurately. By integrating VFD-based diagnostics into NDT, industries can develop scalable, cost-effective, intelligent testing and maintenance solutions that improve reliability and asset management in modern systems. Full article

The thermoplastic composite pipe (TCP) manufacturing process introduces defects that impact performance, such as voids, misalignment, and delamination. Consequently, there is an increasing demand for effective non-destructive testing (NDT) techniques to assess the influence of these manufacturing defects on TCP. The objective is to identify and quantify internal defects at a microscale, thereby improving quality control. A combination of methods, including NDT, has been employed to achieve this goal. The density method is used to determine the void volume fraction. Microscopy and void analysis are performed on pristine samples using optical micrography and scanning electron microscopy (SEM), while advanced techniques like X-ray computer tomography (XCT) and ultrasonic inspections are also applied. The interlayer between the reinforced and inner layers showed good consolidation, though a discontinuity was noted. Microscopy results confirmed solid wall construction, with SEM aligning with the XY axis slice, showing predominant fibre orientation around ±45° and ±90°, and deducing the placement orientation to be ±60°. Comparing immersion, 2D microscopy, and XCT methods provided a comparative approach, even though they could not yield precise void content values. The analysis revealed a void content range of 0–2.2%, with good agreement between microscopy and Archimedes’ methods. Based on XCT and microscopy results, an increase in void diameter at constant volume increases elongation and reduces sphericity. Both methods also indicated that most voids constitute a minority of the total void fraction. To mitigate manufacturing defects, understanding the material’s processing window is essential, which can be achieved through comprehensive material characterization of TCP materials. Full article

The non-destructive geophysical testing method ground penetrating radar (GPR), along with satellite image and air photo assessment, a review of the existing literature sources, and Holocaust survivor testimony, was used to document the location of potential mass graves in Alytus, Lithuania. In World War II, six million Jews were murdered, as were as many as five million other victims of Nazi Germany’s orchestrated persecution. In the summer of 1941, 8030 Jews (4.70 percent of Lithuania’s Jewish population) lived in Alytus County, where the town of Alytus is located. It is estimated that over 8000 Jews were murdered in Alytus County, including nearly the entire Jewish population of the town of Alytus. The murder of Jews from Alytus County accounts for approximately 4.2% of the total number of Lithuanian Jews killed in the Holocaust. Survivor testimony indicates that several thousand Jews from both the town and county were murdered and buried in the Vidzgiris Forest about 1000 m from the town center. In 2022, field reconnaissance at locations in the forest, which appeared to be disturbed in a 1944 German Luftwaffe air photograph, indicated that these disturbances were associated with natural geomorphic processes and not the Holocaust. Analysis of GPR data that was collected using a pulseEKKO Pro 500-megahertz groundpenetrating radar (GPR) system in 2022 in the vicinity of monuments erected in the forest to memorialize mass graves indicates that no mass graves were directly associated with these monuments. The 1944 air photograph contained two roads that traversed through and abruptly ended in the forest, which was the impetus for detailed field reconnaissance in that area. A segment of a 150 m long linear surface feature found in the forest was assessed using GPR, and based on the profile that was generated, it was determined that this feature is possibly a segment of a much more extensive mass grave. Testimony of a Holocaust survivor stated that as many as three burial trenches exist in this portion of the forest. Additional research using non-destructive GPR technology, air photograph and satellite image assessment, and the existing literature and testimony-based data are required for the Vidzgiris Forest to better define these and other potential mass graves and other Holocaust-related features. Full article

Radar polarimetric imaging for non-destructive testing is a powerful and flexible tool that can be used to enhance the detection of internal structures. In this study, reinforced autoclaved aerated concrete (RAAC) is measured using a polarimetric system in three different acquisition modes—two downward-looking and one sideways-looking configurations, each at a different height. Each acquisition mode is compared and new polarisation states are created using the principle of polarisation synthesis. Images of the internal structures are created using a 3D imaging algorithm, which are used for the analysis. The comparison between acquisition modes demonstrates that using a higher lift-off and polarisation synthesis could offer more flexible operation in the field, allowing the use of handheld detectors and drone-based systems for inaccessible areas. Additionally, the sideways-looking data captured both horizontal and vertical reinforcement and were detected within a single polarisation channel; this configuration also has reduced clutter from the air–concrete boundary, providing a viable option for single polarisation systems. Full article

This review examines the essential application of non-destructive testing (NDT) techniques in assessing the integrity and damage of composite materials used in aerospace engineering, focusing on polymer matrix composites (PMCs), metal matrix composites (MMCs), and ceramic matrix composites (CMCs). As these materials increasingly replace traditional metallic and alloy components due to their advantageous properties, such as light weight, high strength, and corrosion resistance, ensuring their structural integrity becomes paramount. Here, various NDT techniques were described in detail, including ultrasonic, radiographic, and acoustic emission, among others, highlighting their significance in identifying and evaluating damages that are often invisible, yet critical, to parts safety. It stresses the need for innovation in NDT technologies to keep pace with the evolving complexity of composite materials and their applications. The review underscores the ongoing challenges and developments in NDT, advocating for enhanced techniques that provide accurate, reliable, and timely assessments to ensure the safety and durability of aerospace components. This comprehensive analysis not only illustrates current capabilities but also directs future research pathways for improving NDT methodologies in aerospace material engineering. Full article

The calibration of a universal testing machine (UTM) verifies the accuracy of the system instruments responsible for obtaining force and displacement measurements. This process involves comparing the instrument to equipment that has already been calibrated to a known traceable standard. The limit of accuracy is then certified and the traceability of the measurements is determined. There are several internationally recognized standards that are used to calibrate the cross-head speed and displacement (ASTM E2658 and E2309, respectively), strain and load rate (ASTM E2309), measurement of tension, compression (ASTM E4) and dynamic force (ASTM E467). The current study aims to monitor the calibration status of UTMs through the implementation of acoustic methods. A methodology is developed whereby a reference sample is initially identified with suitable material properties, enabling it to be used continuously. The sample is used simultaneously with acoustic instruments to check its natural frequencies, which enables the monitoring of the UTM calibration status. An algorithm is developed that enables the user to interact with the system, thus forming an interface and helping the user to check the calibration status of the equipment. The entire system is validated to check if the equipment and the inbuilt algorithm can predict the calibration status of the machine. It was found that the geometric constraints imposed on the sample influence the output from the algorithm, and hence correct values should be fed to the system. Our sample never lost its elastic characteristics through continuous use, demonstrating that it can be used to continuously monitor the machine’s status. Full article

Temperature has a significant impact on the operational lifetime of electronic components, as excessive heat can lead to accelerated degradation and ultimately failure. In safety-critical applications, it is important that real-time monitoring is employed to reduce the risk of system failures and maintain the safety, reliability, and integrity of the connected systems. In the case of printed circuit boards (PCBs), it is often not feasible to install enough sensors to adequately cover all of the temperature sensitive components. In this study, we present a novel method for the temperature monitoring of PCBs using ultrasonic guided waves and machine learning techniques. Our approach utilizes a small number of low-cost, unobtrusive piezoelectric wafer active sensors (PWAS) sensors for propagating ultrasonic guided waves across a PCB. Through interaction with board features, the temperature of components can be predicted using multi-output regression algorithms. Our technique has been applied to three different PCBs, each with five hotspot positions, achieving an RMSE of <3.5 °C and $R^2$ > 0.95 in all three cases. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article