Search Results (64)

This study is based on the laser-induced thermal-crack propagation (LITP) technology, focusing on the issues of deviation and thermal damage during the transverse crack propagation process, with the aim of achieving high-purity, non-destructive, and high-precision cutting of glass. A 50 W, 1064 nm fiber laser is used for S-pattern scanning cutting of soda–lime glass. A moving heat source model is established and analyzed via MATLAB R2022a numerical simulation. Combined with the ABAQUS 2019 software, the relationships among temperature field, stress field, crack propagation, and deviation during laser-induced thermal crack cutting are deeply explored. Meanwhile, laser thermal fracture experiments are also carried out. A confocal microscope detects glass surface morphology, cross-sectional roughness and hardness under different heat flux densities (HFLs), determining the heat flux density threshold affecting the glass surface quality. Through a comprehensive study of theory, simulation, and experiments, it is found that with an increase in the HFL value of the material, the laser-induced thermal crack propagation can be divided into four stages. When the heat flux density value is in the range of 47.2 to 472 W/m², the glass substrate has good cross-sectional characteristics. There is no ablation phenomenon, and the surface roughness of the cross-section is lower than 0.15 mm. The hardness decreases by 9.19% compared with the reference value. Full article

Eggshell quality inspection plays a pivotal role in enhancing the commercial value of poultry eggs and ensuring their safety. It effectively enables the screening of high-quality eggs to meet consumer demand for premium egg products. This paper analyzes the surface characteristics, ultrastructure, and mechanical properties of poultry eggshells. It systematically reviews current advances in eggshell quality inspection technologies and compares the suitability and performance of techniques for key indicators, including shell strength, thickness, spots, color, and cracks. Furthermore, the paper discusses challenges in non-destructive testing, including individual egg variations, species differences, hardware precision limitations, and inherent methodological constraints. It summarizes commercially available portable and online non-destructive testing equipment, analyzing core challenges: the cost–accessibility paradox, speed–accuracy trade-off, algorithm interference impacts, and the technology–practice gap. Additionally, the paper explores the potential application of several emerging technologies—such as tactile sensing, X-ray imaging, laser-induced breakdown spectroscopy, and fluorescence spectroscopy—in eggshell quality inspection. Finally, it provides a comprehensive outlook on future research directions, offering constructive guidance for subsequent studies and practical applications in production. Full article

This study presents a comprehensive bibliometric analysis of research trends in evaluating the mechanical properties of timber structures, with a particular emphasis on the modulus of elasticity (MOE) assessed through non-destructive testing (NDT) methods, especially ultrasonic waves. Using VOSviewer software to analyze 129 Scopus-indexed publications, the analysis reveals a marked increase in research activity since the early 2000s and the formation of distinct thematic clusters. The keyword ’non-destructive examination’ consistently emerges as the dominant term, underscoring a sustained and focused scientific interest in this field. Despite this growth, significant gaps remain, notably the lack of standardized methodologies and limited application of ultrasonic NDT techniques for in-service timber structures. This underscores the urgent need for targeted research efforts, including integrating machine learning with ultrasonic analysis, developing standardized testing protocols, exploring hybrid diagnostic approaches, and extending ultrasonic methods to aged and recycled timber. Furthermore, advancing portable, in-situ ultrasonic systems is essential to enable real-time, field-based assessments. This study not only maps the current research landscape but also highlights strategic opportunities to improve the accuracy, reliability, and sustainability of timber mechanical property evaluations, thereby supporting the advancement of timber engineering. Full article

Chlorophyll content is a critical indicator of the physiological status and fruit quality of pear trees, with Soil Plant Analysis Development (SPAD) values serving as an effective proxy due to their advantages in rapid and non-destructive acquisition. However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. The results demonstrated that (1) both spectral reflectance and vegetation indices exhibited significant correlations with SPAD values, indicating strong retrieval potential; (2) the OIA model consistently outperformed individual algorithms, achieving the highest accuracy when using the combined feature scheme; (3) among the phenological stages, the fruit-enlargement stage yielded the best retrieval performance, with R² values of 0.740 and 0.724 for the training and validation sets, respectively. This study establishes a robust SPAD retrieval framework that integrates multi-source features and multiple models, enhancing prediction accuracy across different growth stages and providing technical support for intelligent orchard monitoring and precision management. Full article

Non-destructive testing (NDT) and non-destructive evaluation (NDE) are essential tools for ensuring the structural integrity, safety, and reliability of critical systems across the aerospace, civil infrastructure, energy, and advanced manufacturing sectors. As engineered materials evolve into increasingly complex architectures such as fiber-reinforced polymers, fiber–metal laminates, sandwich composites, and functionally graded materials, traditional NDT techniques face growing limitations in sensitivity, adaptability, and diagnostic reliability. This comprehensive review presents a multi-dimensional classification of NDT/NDE methods, structured by physical principles, functional objectives, and application domains. Special attention is given to hybrid and multi-material systems, which exhibit anisotropic behavior, interfacial complexity, and heterogeneous defect mechanisms that challenge conventional inspection. Alongside established techniques like ultrasonic testing, radiography, infrared thermography, and acoustic emission, the review explores emerging modalities such as capacitive sensing, electromechanical impedance, and AI-enhanced platforms that are driving the future of intelligent diagnostics. By synthesizing insights from the recent literature, the paper evaluates comparative performance metrics (e.g., sensitivity, resolution, adaptability); highlights integration strategies for embedded monitoring and multimodal sensing systems; and addresses challenges related to environmental sensitivity, data interpretation, and standardization. The transformative role of NDE 4.0 in enabling automated, real-time, and predictive structural assessment is also discussed. This review serves as a valuable reference for researchers and practitioners developing next-generation NDT/NDE solutions for hybrid and high-performance structures. Full article

The colour degradation of murals presents a significant challenge in the conservation of architectural heritage. Previous research has often concentrated on localized pigment changes while paying insufficient attention to the interaction between colour variation and indoor environmental conditions. Although non-destructive analytical techniques are widely used in heritage studies, their integrated application in combination with colourimetry has been limited, particularly in the context of Tibetan Buddhist murals in highland continental climates. This study investigates the murals of Liuli Hall in Meidai Lamasery, Inner Mongolia, as a representative case. We employed a comprehensive methodology that combines non-destructive analytical tools, gas chromatography–mass spectrometry, and quantitative colour analysis to examine pigment composition, binding material, and surface deterioration. Through joint analysis using the CIE Lab and CIE LCh colour space systems, we quantified mural colour changes and explored their correlation with material degradation and environmental exposure. The pigments identified include cinnabar, atacamite, azurite, and chalk, with animal glue and drying oils as binding materials. Colourimetric results revealed pronounced yellowing on the east and west walls, primarily caused by the ageing of organic binders. In contrast, a notable reduction in brightness on the south wall was attributed to dust accumulation. These findings support tailored conservation measures such as regular surface cleaning for the south wall and antioxidant stabilization treatments for the east and west walls. Initial cleaning efforts proved effective. The integrated approach adopted in this study provides a replicable model for mural diagnostics and conservation under complex environmental conditions. Full article

The present study explores a multi-analytical non-invasive approach based on the application of terahertz continuous wave (THz-CW) spectroscopy for the non-invasive characterization of historically produced synthetic copper silicate pigments. For the first time, Han Blue, Han Purple and Egyptian Blue were examined within the THz spectral region using a compact and portable THz-CW spectrometer. The three pigments exhibit distinct absorption features, which facilitate the differentiation of molecular structures within the same chemical and mineralogical category. Moreover, the same compound was analyzed using Energy Dispersive X-Ray Fluorescence (ED-XRF) to determine its elemental composition, alongside Fiber Optics Reflectance Spectroscopy (FORS) in the range 350–2500 nm, providing crucial insights into its optical properties and molecular structure. To the best of the authors’ knowledge, the present study presents the first spectra for these copper silicates at these wavelengths, thereby expanding the shortwave infrared spectral database of Cultural Heritage materials. This synergistic approach enables a comprehensive characterization, offering a deeper understanding of the compounds’ chemical nature and paving the way for potential applications in the Cultural Heritage domain. Furthermore, the findings underscore the potential of THz-CW spectroscopy as an innovative and effective tool for Cultural Heritage research, providing a non-destructive method to investigate artistic materials. Full article

This paper provides a comprehensive overview of the state of the art non-destructive methods for detecting plant biochemical traits through spectral imaging of leafy greens. It offers insights into the various detection techniques and their effectiveness. The review emphasizes the algorithms used for spectral data analysis, highlighting advancements in computational methods that have contributed to improving detection accuracy and efficiency. This systematic review, following the PRISMA 2020 guidelines, explores the applications of non-destructive measurements, techniques, and algorithms, including hyperspectral imaging and spectrometry for detecting a wide range of chemical compounds and elements in lettuce, basil, and spinach. This review focuses on studies published from 2019 onward, focusing on the detection of compounds such as chlorophyll, carotenoids, nitrogen, nitrate, and anthocyanin. Additional compounds such as phosphorus, vitamin C, magnesium, glucose, sugar, water content, calcium, soluble solid content, sulfur, and pH are also mentioned, although they were not the primary focus of this study. The techniques used are showcased and highlighted for each compound, and the accuracies achieved are presented to demonstrate effective detection. Full article

Additive manufacturing (AM) has revolutionized production across industries, yet persistent challenges in defect detection and process reliability necessitate advanced in situ monitoring solutions. While non-destructive evaluation (NDE) techniques such as X-ray computed tomography, thermography, and ultrasonic testing have been widely adopted, the critical role of 3D surface topographic monitoring remains underutilized for real-time anomaly detection. This work comprehensively reviews the 3D surface monitoring of AM processes, such as Laser powder bed fusion, directed energy deposition, material extrusion, and material jetting, highlighting the current state and challenges. Furthermore, the article discusses the state-of-the-art advancements in closed-loop feedback control systems, sensor fusion, and machine learning algorithms to integrate 3D surface data with various process signatures to dynamically adjust laser parameters and scan strategies. Guidance has been provided on the best 3D monitoring technique for each of the AM processes. Motivated by manufacturing labor shortages, the high skill required to operate and troubleshoot some of these additive manufacturing techniques, and zero-defect manufacturing goals, this paper also explores the metamorphosis towards autonomous AM systems and adaptive process optimization and explores the role and importance of real-time 3D monitoring in that transition. Full article

(1) Background: The purpose of this review is to explore how advanced sensor technologies and AI-driven methods, like machine learning and image processing, are shaping non-destructive imaging (NDI) systems. NDI plays a vital role in ensuring the strength and reliability of composite materials. Recent advancements in sensor technologies and AI-driven methods, such as machine learning and image processing, have opened up new ways to improve NDI systems, offering exciting opportunities for better performance. (2) Methods: This review takes a close look at how advanced sensor technologies and machine learning techniques are being integrated into NDI systems. The review evaluates how effective these technologies are at detecting defects and examines their strengths, limitations, and challenges. (3) Results: Combining sensor technologies with AI methods has shown a clear boost in defect detection accuracy and efficiency. However, challenges like high computational requirements and integration costs remain. Despite these hurdles, the potential for these technologies to revolutionize NDI systems is significant. (4) Conclusions: By synthesizing the latest research, this review offers a comprehensive understanding of how sensor technologies are enhancing NDI. The findings highlight their importance for improving defect detection and their broader impact on research and industry, while also pointing out areas where further development is needed for future growth. Full article

The governing Partial Differential Equation (PDE) for wave propagation or the wave equation involves multi-scale and multi-dimensional oscillatory phenomena. Wave PDE challenges traditional computational methods due to high computational costs with rigid assumptions. The advent of scientific machine learning (SciML) presents a novel paradigm by embedding physical laws within neural network architectures, enabling efficient and accurate solutions. This study explores the evolution of SciML approaches, focusing on PINNs, and evaluates their application in modeling acoustic, elastic, and guided wave propagation. PINN is a gray-box predictive model that offers the strong predictive capabilities of data-driven models but also adheres to the physical laws. Through theoretical analysis and problem-driven examples, the findings demonstrate that PINNs address key limitations of traditional methods, including discretization errors and computational inefficiencies, while offering robust predictive capabilities. Despite current challenges, such as optimization difficulties and scalability constraints, PINNs hold transformative potential for advancing wave propagation modeling. This comprehensive study underscores the transformative potential of PINN, followed by recommendations on why and how it could advance elastic, acoustic, and guided wave propagation modeling and sets the stage for future research in the field of Structural Health Monitoring (SHM)/Nondestructive Evaluation (NDE). Full article

The sustainable development and preservation of natural resources have highlighted the critical need for the effective maintenance of civil engineering infrastructures. Recent advancements in technology and data digitization enable the acquisition of data from sensors on structures like bridges, tunnels, and energy production facilities. This paper explores “smart” uses of these data to optimize maintenance actions through interdisciplinary approaches, integrating artificial intelligence in civil engineering. Corrosion, a key factor affecting infrastructure health, underscores the need for robust predictive maintenance models. Supervised Machine Learning regression methods, particularly Random Forest (RF) and Artificial Neural Networks (ANNs), are investigated for predicting structural properties based on Non-Destructive Testing (NDT) data. The dataset includes various measurements such as ultrasonic, electromagnetic, and electrical on concrete samples. This study compares the performances of RF and ANN in predicting concrete characteristics, like compressive strength, elastic modulus, porosity, density, and saturation rate. The results show that, while both models exhibit strong predictive capabilities, RF generally outperforms ANN in most metrics. Additionally, SHapley Additive exPlanation (SHAP) provides insights into model decisions, ensuring transparency and interpretability. This research emphasizes the potential of integrating Machine Learning with empirical and mechanical methods to enhance infrastructure maintenance, providing a comprehensive framework for future applications. Full article

Concrete structures in marine environments are subjected to severe conditions that significantly compromise their durability and service life. Exposure to chloride penetration, sulfate attack, and physical erosion accelerates deterioration, leading to extensive maintenance requirements and high associated costs. To address these challenges, significant advancements in surface coatings and inspection technologies have been developed to enhance the longevity of concrete structures. This review examines recent progress in protective surface coatings that mitigate environmental damage and explores state-of-the-art inspection techniques for assessing structural integrity. By providing a comprehensive analysis of innovative materials, coating applications, and non-destructive evaluation methods, this paper aims to equip researchers and industry professionals with effective strategies for preserving concrete infrastructure in marine environments. Full article

The rapid adoption of electric vehicles (EVs) has increased the demand for efficient methods to assess the state of health (SoH) of lithium-ion batteries (LIBs). Accurate and prompt evaluations are essential for safety, battery life extension, and performance optimization. While traditional techniques such as electrochemical impedance spectroscopy (EIS) are commonly used to monitor battery degradation, acoustic emission (AE) analysis is emerging as a promising complementary method. AE’s sensitivity to mechanical changes within the battery structure offers significant advantages, including speed and non-destructive assessment, enabling evaluations without disassembly. This capability is particularly beneficial for diagnosing second-life batteries and streamlining decision-making regarding the management of used batteries. Moreover, AE enhances diagnostics by facilitating early detection of potential issues, optimizing maintenance, and improving the reliability and longevity of battery systems. Importantly, AE is a non-destructive technique and belongs to the passive method category, as it does not introduce any external energy into the system but instead detects naturally occurring acoustic signals during the battery’s operation. Integrating AE with other analytical techniques can create a comprehensive tool for continuous battery condition monitoring and predictive maintenance, which is crucial in applications where battery reliability is vital, such as in EVs and energy storage systems. This review not only examines the potential of AE techniques in battery health monitoring but also underscores the need for further research and adoption of these techniques, encouraging the academic community and industry professionals to explore and implement these methods. Full article

Infrastructure maintenance is critical to ensuring public safety and the longevity of essential structures. Nondestructive Evaluation (NDE) techniques allow for infrastructure inspection without causing damage. Computer vision has emerged as a powerful tool in this domain, providing automated, efficient, and accurate solutions for defect detection, structural monitoring, and real-time analysis. This review explores the current state of computer vision in NDE, discussing key techniques, applications across various infrastructure types, and the integration of deep learning models such as convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid models. The review also highlights challenges, including data availability and scalability. It proposes future research directions, including real-time monitoring and the integration of Artificial Intelligence (AI) with Internet of Things (IoT) devices for comprehensive inspections. Full article