Search Results (50)

This study explores the application of low-power vibrothermography (LVT) for detecting barely visible impact damage (BVID) in carbon fibre-reinforced polymer (CFRP) laminates. Composite specimens with varying impact energies (2.5–20 J) were excited using a single piezoelectric transducer with a nominal centre frequency of 28 kHz, operated at a fixed excitation frequency of 28 kHz. Thermal data were captured using an infrared camera. To enhance defect visibility and suppress background noise, the raw thermal sequences were processed using principal component analysis (PCA) and robust principal component analysis (RPCA). In LVT, RPCA and PCA provided comparable signal-to-noise ratios (SNR), with no consistent advantage for either method across all cases. In contrast, for pulsed thermography (PT) data, RPCA consistently resulted in higher SNR values, except for one sample. The LVT results were further validated by comparison with PT and phased array ultrasonic testing (PAUT) data to confirm the location and shape of detected damage. These findings demonstrate that LVT, when combined with PCA or RPCA, offers a reliable method for identifying BVID and can support safer, more efficient structural health monitoring of composite materials. Full article

Understanding the structural concordance between taxonomic and functional diversity (FD) metrics is essential for improving the ecological interpretation of community patterns in biomonitoring programs. This study evaluated the concordance between taxonomic and FD metrics of benthic macroinvertebrates along a fluvial habitat quality gradient in the Paute River Basin, Ecuador. Macroinvertebrate communities were sampled over six years at twelve sampling points and assessed using four taxonomic metrics: Shannon diversity (H), the Margalef index (D_Mg), family richness (N), and the Andean Biotic Index (ABI). Functional diversity was evaluated using four metrics: weighted functional dendrogram-based diversity (wFDc), Rao’s quadratic entropy (Rao), functional dispersion (FDis), and functional richness (FRic). The fluvial habitat index (FHI) was used as an environmental reference to evaluate diversity metric responses. K-means clustering was independently applied to each metric, and pairwise concordance was quantified using the Measure of Concordance (MoC) and overlap in sampling points groupings across replicates. Most metrics (except FRic and N) showed clear responsiveness to the FHI gradient, confirming their ecological relevance. Strong structural concordance was observed between H and D_Mg and the FD metrics Rao, FDis, and wFDc, showing that these metrics captured similar yet complementary aspects of community organization. In contrast, ABI showed marked sensitivity to the FHI gradient but low concordance with functional metrics, suggesting distinct dimensions of biological integrity not encompassed by trait-based metrics. These findings highlight the value of combining taxonomic and functional metrics to detect both broad and subtle ecological changes. Integrating metrics with differing structural properties and environmental sensitivities can enhance the robustness of freshwater biomonitoring frameworks, especially in systems undergoing ecological transition or habitat degradation. Full article

Recent advancements in machine learning (ML) have led to state-of-the-art performance in various domain-specific tasks, driving increasing interest in its application to non-destructive testing (NDT). Among NDT techniques, phased array ultrasonic testing (PAUT) is an advanced extension of conventional ultrasonic testing (UT). This article provides an overview of recent research advances in ML applied to PAUT, covering key applications such as phased array ultrasonic imaging, defect detection and characterization, and data generation, with a focus on multimodal data processing and multidimensional modeling. The challenges and pathways for integrating the two techniques are examined. Finally, the article discusses the limitations of current methodologies and outlines future research directions toward more accurate, interpretable, and efficient ML-powered PAUT solutions. Full article

The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo (IE), are critically evaluated in terms of their effectiveness in detecting debonding, voids, delaminations, and other defects. Recent technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) in NDT applications, have significantly improved defect characterization, automated inspections, and real-time data analysis. This review highlights AI-driven NDT approaches such as automated crack detection, hybrid NDT frameworks, and drone-assisted thermographic inspections, which enhance accuracy and efficiency in large-scale infrastructure assessments. Additionally, economic considerations and cost–performance trade-offs are analyzed, addressing the feasibility of different NDT methods in real-world FRP-strengthened structures. Finally, the review identifies key research gaps, including the need for standardization in FRP-NDT applications, AI-enhanced defect quantification, and hybrid inspection techniques. By consolidating state-of-the-art research and emerging innovations, this paper serves as a valuable resource for engineers, researchers, and practitioners involved in the assessment, monitoring, and maintenance of FRP-strengthened concrete structures. Full article

Lithium-ion batteries (LIBs) are widely used in electric vehicles and energy storage systems, making accurate state transition monitoring a key research topic. This paper presents a characterization method for large-format LIBs based on phased-array ultrasonic technology (PAUT). A finite element model of a large-format aluminum shell lithium-ion battery is developed on the basis of ultrasonic wave propagation in multilayer porous media. Simulations and comparative analyses of phased array ultrasonic imaging are conducted for various operating conditions and abnormal gas generation. A 40 Ah ternary lithium battery (NCMB) is tested at a 0.5C charge-discharge rate, with the state of charge (SOC) and ultrasonic data extracted. The relationship between ultrasonic signals and phased array images is established through simulation and experimental comparisons. To estimate the SOC, a fully connected neural network (FCNN) model is designed and trained, achieving an error of less than 4%. Additionally, phased array imaging, which is conducted every 5 s during overcharging and overdischarging, reveals that gas bubbles form at 0.9 V and increase significantly at 0.2 V. This research provides a new method for battery state characterization. Full article

This review paper aims to understand the current processing plant maintenance systems and further identify on-stream phased array corrosion mapping (PACM) to reduce turnaround maintenance (TAM) activity during plant operations. Reducing the TAM duration and extending the TAM interval are common goals of most researchers. Thus, a detailed review was performed to understand the maintenance systems and the problems faced. Furthermore, a review of the current PACM application and the possibility of applying it during on-stream inspection was also performed. PACM has better detectability for localized corrosion, and the results can be obtained for a range of thicknesses, which is the main advantage of this method. However, applying PACM during on-stream inspections at elevated temperatures presents challenges owing to the limitations of the ultrasonic properties and increased probe contact. Future research is needed to evaluate the effectiveness of PACM on piping systems that can be utilized for inspection during plant operation at elevated temperatures. This will enable the detection of general and localized corrosion in common materials, thereby reducing the TAM duration and extending TAM intervals. Detecting and monitoring corrosion growth without shutdown is critical for ensuring the safety and reliability of the processing plants. This literature review provides a more precise direction for future research to address these challenges and to advance the field of on-stream corrosion monitoring. Full article

Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis. Full article

Magnetite nanoparticles (NPs) possess properties that make them suitable for a wide range of applications. In recent years, interest in the synthesis of magnetite NPs and their surface functionalization has increased significantly, especially regarding their application in biomedicine such as for controlled and targeted drug delivery. There are several conventional methods for preparing magnetite NPs, all of which mostly utilize Fe(iii) and Fe(ii) salt precursors. In this study, we present a microwave hydrothermal synthesis for the precipitation of magnetite NPs at temperatures of 200 °C for 20 min and 260 °C for 5 min, with only iron(iii) as a precursor utilizing chamomile flower extract as a stabilizing, capping, and reducing agent. Products were characterized using FTIR, PXRD, SEM, and magnetometry. Our analysis revealed significant differences in the properties of magnetite NPs prepared with this approach, and the conventional two-precursor hydrothermal microwave method (sample MagH). FTIR and PXRD analyses confirmed coated magnetite particles. The temperature and magnetic-field dependence of magnetization indicate their superparamagnetic behavior. Importantly, the results of our study show the noticeable cytotoxicity of coated magnetite NPs—toxic to carcinoma cells but harmless to healthy cells—further emphasizing the potential of these NPs for biomedical applications. Full article

This paper presents a novel approach for preload measurement of bolted connections, specifically tailored for offshore wind applications. The proposed method combines robotics, Phased Array Ultrasonic Testing (PAUT), nonlinear acoustoelasticity, and Finite Element Analysis (FEA). Acceptable defects, below a pre-defined size, are shown to have an impact on preload measurement, and therefore conducting simultaneous defect detection and preload measurement is discussed in this paper. The study demonstrates that even slight changes in the orientation of the ultrasonic transducer, the non-automated approach, can introduce a significant error of up to 140 MPa in bolt stress measurement and therefore a robotic approach is employed to achieve consistent and accurate measurements. Additionally, the study emphasises the significance of considering average preload for comparison with ultrasonic data, which is achieved through FEA simulations. The advantages of the proposed robotic PAUT method over single-element approaches are discussed, including the incorporation of nonlinearity, simultaneous defect detection and stress measurement, hardware and software adaptability, and notably, a substantial improvement in measurement accuracy. Based on the findings, the paper strongly recommends the adoption of the robotic PAUT approach for preload measurement, whilst acknowledging the required investment in hardware, software, and skilled personnel. Full article

Thermal fatigue cracks occurring in pipes in nuclear power plants pose a high degree of risk. Thermal fatigue cracks are generated when the thermal fatigue load caused by local temperature gradients is repeatedly applied. The flaws are mainly found in welds, owing to the effects of stress concentration caused by the material properties and geometric shapes of welds. Thermal fatigue pipes are classified as targets of risk-informed in-service inspection, for which ultrasonic testing, a volumetric non-destructive testing method, is applied. With the advancement of ultrasonic testing techniques, various studies have been conducted recently to apply the phased array ultrasonic testing (PAUT) method to the inspection of thermal fatigue cracks occurring on pipes. A quantitative reliability analysis of the PAUT method must be performed to apply the PAUT method to on-site thermal fatigue crack inspection. In this study, to evaluate the quantitative reliability of the PAUT method for thermal fatigue cracks, we fabricated crack specimens with the thermal fatigue mechanism applied to the pipe welds. We performed a round-robin test to collect PAUT data and determine the validity of the detection performance (probability of detection; POD) and the error in the sizing accuracy (root-mean-square error; RMSE) evaluation. The analysis results of the POD and sizing performance of the length and depth of thermal fatigue cracks were comparatively evaluated with the acceptance criteria of the American Society of Mechanical Engineers Code to confirm the effectiveness of applying the PAUT method. Full article

The objective of this research is to analyze how family farming contributes to food sovereignty; the Guarainag parish of the Paute canton in the province of Azuay-Ecuador is taken as a case of study. This work responds to the necessity to explain the elements that impact food sovereignty in the existing food crisis in Latin America and specifically in Ecuador in search of self-sufficiency for healthy food products and people’s own local culture. For this purpose, a Food Sovereignty Index was constructed through ten quantitative and qualitative indicators. The research has a correlational and explanatory scope; quantitative methods were used to measure food sovereignty through a binary logit regression model, which provided an answer to the hypothesis of the research, which consisted of testing the influence of family farming on food sovereignty. Furthermore, to collect the information, a survey was applied to 372 small farmers with the support of digital mapping and the Kobol Tulboox software version 1.27.3. The result was a Food Sovereignty Index of 59.79%, which, according to the scale used, places the territory in a high average. In addition, the hypothesis was verified, concluding that there is a direct relationship among the following elements of family farming such as number of household members, family labor, group of products, type of animals, tillage technology, natural fertilizer, and altitudinal levels with food sovereignty. For future research, it is recommended that the variable of climate change has to be incorporated in order to observe its impact on food sovereignty. Full article

Petrochemical plants use on-stream inspection often to detect and monitor the corrosion on the equipment and piping system. Compared to ultrasonic thickness gauging and pulse-echo A-scan, phased array corrosion mapping has better coverability and can scan a large area to detect general and localized corrosion. This paper’s objective is to obtain documentary evidence for the accuracy of corrosion detection from 30 °C to 250 °C on A36 low-carbon steel by carrying out simulation experiments every 10 °C step. A minimum of three sets of phased array corrosion mapping data in each temperature were collected to study and evaluate the detectability. The data evidence could enhance the confidence level of the plant’s end users in using phased array mapping in the future during inspections. The experiments were found to be insufficiently thorough despite addressing the initial concerns, leaving more area for discussion in further studies, such as expanding the investigation to thicker carbon steel, stainless steel, and wedge materials. Full article

Anchor bolts are often used for fixing information boards, supports, and soundproof walls in various facilities. Corrosion of anchor bolts and fatigue cracks occur frequently due to the various external environments, and visual inspection and hammering inspection are mainly used. In visual inspection, it is difficult to confirm corrosion or fatigue cracks of anchor bolts in the area where foundations, nuts, and base plates are installed. Additionally, the hammering inspection is easily affected by the surrounding environment and the subjective reaction of the tester. Therefore, it is necessary to develop a method that can easily and accurately detect defects such as cracks and corrosion occurring in anchor bolts installed in road facilities using non-destructive testing techniques. In this paper, the possibility and reliability of anchor bolt defects such as corrosion and cracks were experimentally verified by applying ultrasonic inspection among non-destructive inspection techniques for anchor bolt maintenance. Full article

Ultrasonic non-destructive testing (UT) is a proficient method for detecting damage in composite materials; however, conventional manual testing procedures are time-consuming and labor-intensive. We propose a semi-automated defect segmentation methodology employing a convolutional neural network (CNN) on 3D ultrasonic data, facilitated by the fusion of X-ray computed tomography (XCT) and Phased-Array Ultrasonic Testing (PAUT) data. This approach offers the ability to develop supervised datasets for cases where UT techniques inadequately assess defects and enables the creation of models with genuine defects rather than artificially introduced ones. During the training process, we recommend processing the 3D volumes as a sequence of 2D slices derived from each technique. Our methodology was applied to segment porosity, a common defect in composite materials, for which characteristics such as void size and shape remain immeasurable via UT. Precision, recall, F1 score, and Intersection over Union (IoU) metrics were used in the evaluation. The results of the evaluation show that the following challenges have to be faced for improvement: (i) achieving accurate 3D registration, (ii) discovering suitable similar keypoints for XCT and UT data registration, (iii) differentiating ultrasonic echoes originating from porosity versus those related to noise or microstructural features (interfaces, resin pockets, fibers, etc.), and, (iv) single out defect echoes located near the edges of the component. In fact, an average F1 score of 0.66 and IoU of 0.5 were obtained. Full article

Plant extracts are increasingly being examined in the corrosion inhibition of metal and alloys in various environments due to their potent antioxidant properties. The use of Artemisia annua L. aqueous extract (AAE) as an aluminium alloy 5083 (ALA) corrosion inhibitor in artificial seawater (ASW) was investigated using electrochemical tests and spectroscopy tools, while the active biocompounds found in AAE were analyzed using high-performance liquid chromatography (HPLC). Electrochemical results showed that AAE acts as an anodic inhibitor through the physisorption (ΔG ≈ –16.33 kJ mol⁻¹) of extract molecules on the ALA surface, thus reducing the active sites for the dissolution of the alloy in ASW. Fourier-transform infrared spectra confirmed that phenolic acids found in AAE formed the surface layer that protects ALA against the corrosive marine environment, while HPLC analysis confirmed that the main phytoconstituents of AAE were chlorogenic acid and caffeic acid. The inhibition action of phenolic acids and their derivatives found in the AAE was based on the physisorption of caffeic acid on the ALA surface, which improved physicochemical properties of the barrier film and/or conversion of Al³⁺ to elemental aluminium by phenolic acids as reducens, which slowed down the diffusion rate of Al³⁺ to or from the ALA surfaces. The protective effect of the surface layer formed in the presence of AAE against ASW was also confirmed by inductively coupled plasma–optical emission spectrometry (ICP-OES) whereby the measured concentration of Al ions after 1 h of immersion of ALA in the pure ASW was 15.30 μg L⁻¹ cm⁻², while after the addition of 1 g L⁻¹ AAE, the concentration was 3.09 μg L⁻¹ cm⁻². Full article