Search Results (3,680)

An accelerometer mounting technique has large implications on the frequency range and accuracy of the measurement, with stiffness and the mass relative to the monitored structure as the primary concerns. The International Organization for Standardization (ISO) gives an extensive list in 5348:2021, detailing mounting methods, and provides recommendations for testing mounts that are not specifically defined. In the nuclear industry on the laboratory scale, there is a need for vibration measurements for predictive maintenance and process monitoring that are nondestructive and capable of working in high-temperature environments. Commercial adhesive products with easy application and removal were tested as nondestructive methods, while quick mounts to a commonly used aluminum frame were tested as nondestructive and have potential applicability in high-temperature environments. The sinusoidal excitation method was used, measuring frequencies from 50 Hz to 10 kHz in one-third octave band intervals, utilizing three accelerometers and comparing the results to those obtained with the stud-mounting method. Using the lowest ±3 dB threshold across each accelerometer, foam dots and poster strips were not successful, and foam tapes were accurate up to 2000 Hz, hose clamps and zip ties up to 800 Hz, and a custom 3D printed mount up to 1000 Hz. Knowing the limitations of each mounting technique allows for accurate measurements within the appropriate range. Full article

To address the issue of insufficient contrast in conventional X-ray absorption imaging for biological soft tissues and weakly absorbing materials, this paper proposes a beam tracking X-ray phase-contrast imaging system using a conventional X-ray source. A periodic pinhole array mask is placed between the X-ray source and the sample to spatially modulate the X-ray beam, dividing it into multiple independent sub-beams. Each sub-beam is deflected due to the modulation effect of the sample, resulting in slight positional shifts in the intensity patterns formed on the detector. The experiments employ an X-ray source with a 400 μm focal spot and use a two-dimensional step-scanning approach to acquire image sequences of various samples. The experimental results show that this method can extract the edge profile and structural changes in the samples to some extent, and it demonstrates good contrast and detail recovery under weak absorption conditions. These results suggest that this method has certain application potential in material inspection, non-destructive testing, and related fields. Full article

Automated quality control is critical in modern manufacturing, especially for metallic cast components, where fast and accurate surface defect detection is required. This study evaluates classical Machine Learning (ML) algorithms using extracted statistical parameters and deep learning (DL) architectures including ResNet50, Capsule Networks, and a 3D Convolutional Neural Network (CNN3D) using 3D image inputs. Using the Dataset Original, ML models with the selected parameters achieved high performance: RF reached 99.4 ± 0.2% precision and 99.4 ± 0.2% sensitivity, GB 96.0 ± 0.2% precision and 96.0 ± 0.2% sensitivity. ResNet50 trained with extracted parameters reached 98.0 ± 1.5% accuracy and 98.2 ± 1.7% F1-score. Capsule-based architectures achieved the best results, with ConvCapsuleLayer reaching 98.7 ± 0.2% accuracy and 100.0 ± 0.0% precision for the normal class, and 98.9 ± 0.2% F1-score for the affected class. CNN3D applied on 3D image inputs reached 88.61 ± 1.01% accuracy and 90.14 ± 0.95% F1-score. Using the Dataset Expanded with ML and PCA-selected features, Random Forest achieved 99.4 ± 0.2% precision and 99.4 ± 0.2% sensitivity, K-Nearest Neighbors 99.2 ± 0.0% precision and 99.2 ± 0.0% sensitivity, and SVM 99.2 ± 0.0% precision and 99.2 ± 0.0% sensitivity, demonstrating consistent high performance. All models were evaluated using repeated train-test splits to calculate averages of standard metrics (accuracy, precision, recall, F1-score), and processing times were measured, showing very low per-image execution times (as low as $3.69\times {10}^{-4}$ s/image), supporting potential real-time industrial application. These results indicate that combining statistical descriptors with ML and DL architectures provides a robust and scalable solution for automated, non-destructive surface defect detection, with high accuracy and reliability across both the original and expanded datasets. Full article

Infrared thermography non-destructive testing technology has been widely used in the defect detection of composite structures due to its advantages, including non-contact operation, rapidity, low cost, and high precision. In this study, a laser-line scanning system combined with an infrared thermography was developed, along with a corresponding dynamic sequence image reconstruction method, enabling rapid localization of surface damages. Then, high-precision quantitative characterization of defect morphology in reconstructed images was achieved by integrating an edge gradient detection algorithm. The reconstruction method was validated through finite element simulations and experimental studies. The results demonstrated that the laser-line scanning thermography effectively enables both rapid localization of surface damages and precise quantitative characterization of their morphology. Experimental measurements of ceramic materials indicate that the relative error in detecting crack width is about 6% when the crack is perpendicular to the scanning direction, and the relative error gradually increases when the angle between the crack and the scanning direction decreases. Additionally, an alumina ceramic plate with micrometer-width cracks is inspected by the continuous laser-line scanning thermography. The morphology detection results are completely consistent with the actual morphology. However, limited by the spatial resolution of the thermal imager in the experiment, the quantitative identification of the crack width cannot be carried out. Finally, the proposed method is also effective for detecting surface damage of wrinkles in ceramic matrix composites. It can localize damage and quantify its geometric features with an average relative error of less than 3%, providing a new approach for health monitoring of large-scale ceramic matrix composite structures. Full article

Spectral detection technology offers non-destructive, in situ, and high-speed capabilities, making it widely applicable for detecting biological and chemical samples and quantifying their concentrations. Water resources, essential to life on Earth, are widely distributed across the planet. The application of spectral technology to underwater environments is useful for wide-area water resource monitoring. Although spectral detection technology is well-established, its underwater application presents challenges, including waterproof housing design, power supply, and data transmission, which limit widespread application of underwater spectral detection. Furthermore, underwater spectral detection necessitates the development of compatible computational methods for sample classification or regression analysis. Focusing on underwater spectral detection, this work involved the construction of a suitable hardware system. A compact spectrometer and LEDs (400 nm–800 nm) were employed as the detection and light source modules, respectively, resulting in a compact system architecture. Extensive tests confirmed that the miniaturized design-maintained system performance. Further, this study addressed the estimation of total phosphorus (TP) concentration in water using spectral data. Samples with varying TP concentrations were prepared and calibrated against standard detection instruments. Subsequently, classification algorithms applied to the acquired spectral data enabled the in situ underwater determination of TP concentration in these samples. This work demonstrates the feasibility of underwater spectral detection for future in situ, high-speed monitoring of aquatic biochemical indicators. In the future, after adding UV LED light source, more water quality parameter information can be obtained. Full article

The main property of a steel substrate is the adhesion of its coating, which determines the quality and durability of the adhesive joint. The main objective of the research presented in this article is to evaluate the adhesion of coatings to substrates based on ultrasonic measurements and the determined reflection coefficient |r|. An experiment was carried out on disc samples, not only for ultrasonic measurements but also for the evaluation of the mechanical adhesion of coatings to substrates using the pull-off test. Three different methods of surface preparation of the samples were used: glass beading, surface treatment with P400 sandpaper, and the laser beam treatment. Based on the results, it was found that the best adhesion was obtained for samples with surfaces prepared by the glass-beading process. Reflection coefficient values in the range of 0.61–0.83 corresponded to mechanical adhesion in the range of 1.75–4.56 MPa. The results of the tests provide an important reference for the nondestructive evaluation of coating adhesion to substrates and allow for the estimation of mechanical adhesion based on the values of the reflection coefficient |r|. Full article

This article presents an extensive experimental investigation of the dynamic characteristics of three-arch historical masonry bridges, using Operational Modal Analysis (OMA). The research thoroughly characterizes the dynamic behavior of four representative masonry bridges from the Apulia Region in Southern Italy through detailed experimental campaigns. These campaigns employed calibrated and optimally implemented accelerometric monitoring systems to acquire high-quality dynamic data under controlled excitation and environmental conditions. The selected bridges include the Santa Teresa Bridge in Bitonto, the Roman Bridge in Bovino, the Roman Bridge in Ascoli Satriano and a moderner road bridge on the Provincial Road SP123 in Troia; they span almost two millennia of construction history. The experimental framework incorporated several non-invasive excitation methods, including controlled vehicle passes, instrumented hammer impacts and ambient vibration tests, strategically chosen for optimal signal quality and heritage preservation. This investigation demonstrates the feasibility of capturing the dynamic behavior of these complex and specific historic structures through customized sensor configurations and various excitation methods. The resulting natural frequencies and mode shapes are accurate, robust, and reliable considering the extended data set used, and have allowed a rigorous seismic assessment. Eventually, this comprehensive data set establishes a fundamental basis for understanding and predicting the seismic response of several three-span masonry bridges to accurately identify their long-term resilience and effective conservation planning of these valuable and vulnerable heritage structures. In conclusion, the data comparison enabled the formulation of a predictive equation for the identification of the first natural frequency of bridges from geometric characteristics. Full article

The monitoring of safety and health in lithium-ion batteries (LIBs) presents a significant challenge. Ultrasonic detection techniques fulfil the requirements for high sensitivity and non-destructive evaluation in the safety assessment of these batteries. This study concentrates on the application of electromagnetic acoustic transducer (EMAT) technology for non-destructive battery testing, utilizing non-contact electromagnetic coupling to generate and receive ultrasonic waves. This method addresses the limitations associated with conventional piezoelectric ultrasonic coupling media, thereby facilitating highly reliable assessment of the internal condition of batteries. Specifically, this paper independently designs an EMAT featuring a Halbach magnet array and a butterfly coil. Based on this design, optimization is performed, and the amplitude of the received signal is increased fourfold compared to the pre-optimization configuration. The optimized transducer is employed to evaluate a set of retired batteries with a nominal capacity of 270 Ah. Experimental results demonstrate that batteries exhibiting capacities below 240 Ah produced average signal amplitudes more than 40% lower than those of batteries with higher capacities. This technology provides a non-contact, disassembly-free approach for rapid performance evaluation of batteries and demonstrates potential for effective application in sorting retired battery units. Full article

Dendrobium officinale is an important medicinal and edible plant in China, widely used in the dietary health industry and pharmaceutical field. Due to the different geographical origins and cultivation methods, the nutritional value, medicinal quality, and price of Dendrobium are significantly different, and accurate identification of the origin is crucial. Current origin identification relies on expert judgment or requires costly instruments, lacking an efficient solution. This study proposes a Variational Inference-enabled Data-Efficient Learning (VIDE) model for high-precision, non-destructive origin identification using a small number of image samples. VIDE integrates dual probabilistic networks: a prior network generating latent feature prototypes and a posterior network employing variational inference to model feature distributions via mean and variance estimators. This synergistic design enhances intra-class feature diversity while maximizing inter-class separability, achieving robust classification with limited samples. Experiments on a self-built dataset of Dendrobium officinale samples from six major Chinese regions show the VIDE model achieves 91.51% precision, 92.63% recall, and 92.07% F1-score, outperforming state-of-the-art models. The study offers a practical solution for geographical origin identification and advances intelligent quality assessment in Dendrobium officinale. Full article

With the widespread use of high-density polyethylene (HDPE) pipes in various industrial and municipal applications, ensuring the structural integrity of their joints is crucial. This paper presents a novel defect detection method based on a microwave resonant probe, designed to perform efficient and non-destructive evaluation of butt fusion joints in HDPE pipes. The experimental setup integrates a microwave antenna and resonant cavity to record real-time variations in resonance frequency and S₂₁ magnitude while scanning the pipe surface. This method effectively detects common defects, including cracks, holes, and inclusions, within the butt fusion joints. The results show that the microwave resonant probe exhibits high sensitivity in detecting HDPE pipe defects. It can identify different sizes of cracks and holes, and can distinguish between talc powder and sand particles. This technique offers a promising solution for pipeline health monitoring, particularly for evaluating the quality of welded joints in non-metallic materials. Full article

The quality of mangoes is a crucial factor in both domestic and commercial markets that directly influences consumer satisfaction and economic value. Traditional methods of checking mango quality often involve destructive techniques, which lead to the loss of the fruit in the testing process. This study presents an advanced approach that could predict the quality of mangoes using advance non-destructive methods leveraging machine learning algorithms to predict quality parameters such as ripeness, sweetness and overall freshness without damaging the fruit. In this research, a dataset consisting of various mango samples was collected, with attributes including color, texture, size, weight and acidity levels. Sensors, such as pH sensors (for acidity) and e-nose sensors (for aroma and sweetness detection), were used to gather data, while a combination of machine learning models such as Decision Tree, K-Nearest Neighbors (KNN), and Automated Machine Learning (AutoMLP), Naive Bayes were applied to predict the mangoes’ quality. The accuracy of each model was measured based on its ability to classify mangoes as fresh, ripe, or rotten. The results determine that the AutoMLP model performs the best out of the traditional models, achieving an accuracy of 98.46%, making it the most suitable model for mango quality prediction. The research explains the significance of feature extraction methods, model optimization, and sensor data pretreatment in reaching a high prediction accuracy. Full article

Discrimination between normal (fresh/non-frozen) and frozen-thawed beef is crucial for ensuring food safety. This paper proposed a novel, non-destructive and real-time you only look once for normal and frozen-thawed beef discrimination (YOLO-NF) model using deep learning techniques. The simple, parameter-free attention module (SimAM) and the squeeze and excitation (SE) attention mechanism were introduced to enhance the model’s performance. A total of 1200 beef samples were used, with their images captured by a charge-coupled device (CCD) camera. In the model development, specifically, the training set comprised 3888 images after data augmentation, while the validation set and test set each included 216 original images. Experimental results on the test set showed that the YOLO-NF model achieved precision, recall, F1-Score and mean average precision (mAP) of 95.5%, 95.2%, 95.3% and 98.6%, respectively, significantly outperforming YOLOv7, YOLOv5 and YOLOv8 models. Additionally, gradient-weighted class activation mapping (Grad-CAM) was adopted to interpret the model’s decision basis. Moreover, the model was deployed on the web interface for user convenience, and the discrimination time on the local server was 0.94 s per image, satisfying the requirements for real-time processing. This study provides a promising technique for high-performance and rapid meat quality assessment in food safety monitoring systems. Full article

Non-destructive measurement of leaf size based on leaf length and/or width is a simple, economical, and precise methodology. Leaf morphometric indicators were measured on 55 coffee progenies obtained from intraspecific and interspecific crosses. The estimation of parameters in the models and the testing of hypotheses related to these were performed. The relationships between leaf width and length, the ellipticity index, and leaf size were subsequently analyzed with a partitioning algorithm. The groups were then compared using Hotelling’s T² test. In coffee, the Montgomery model allowed for an adequate estimation of leaf size for each progeny, hybridization type, and grouped data. An α value of 0.67000 for the Montgomery model was consistent. This finding indicates that it is a suitable model for both individual and groups of progenies. The model based on the “principle of similarity” was found to be suitable only on a per-progeny basis. Certain characteristics, such as the leaf width-to-length ratio, ellipticity index, and leaf size, modify the parameter fit to inherent values. Similarly, leaves with a higher width-to-length ratio were the most elliptical for coffee, according to the groupings found. The estimation of coffee leaf size improves if the selected model considers whether they come from specific progenies or groups of progenies. Full article

This research investigates the statistical correlation between Vickers hardness and tensile properties of helical submerged arc welded high-strength low-alloy (HSLA) API X70 pipeline steel. Tensile tests were performed on cross-weld joints from 138 pipe specimens. Vickers hardness measurements were also conducted on 138 samples to evaluate the hardness distribution across the base metal, fusion zone, and heat-affected zone. Results show that the fusion zone exhibits the highest hardness, correlating with enhanced tensile strength (R² = 82%). Linear regression models indicate that base metal hardness significantly influences yield strength (R² = 71%), while moderate negative correlations exist with elongation (R² = 54%). These findings suggest that hardness measurements can serve as a non-destructive predictive tool for tensile properties, improving weld quality and mechanical performance. This research provides empirical models that enhance the application of API X70 in critical engineering applications, improving pipeline safety and reliability. Full article

Stone cultural heritage structures built from pyroclastic rocks are susceptible to deterioration due to their sensitivity to atmospheric processes. Detecting such deterioration and periodically examining it using non-destructive testing (NDT) techniques is one of the most critical measures for ensuring its transmission to future generations. In recent years, assessing the properties of building stones through NDT methods has been widely applied in planning the preservation of stone cultural heritages. In this study, deterioration observed on the interior walls of the Emir Ali Tomb, a structure distinguished from other tombs in the region by its exceptional architecture, was investigated through laboratory tests and NDT techniques, including deep moisture measurement, P-wave velocity, and infrared thermography. It was determined that the monument was constructed from four different types of pyroclastic rock, classified according to their textural and geomechanical characteristics. Using data obtained from in situ tests, NDT distribution maps were generated. The deep moisture, P-wave velocity, and infrared thermography maps revealed that the primary cause of deterioration in the monument was related to capillary water rise. Full article