Search Results (40)

Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs between high-yielding ponded and aerobic rice, (ii) validate the feasibility of using the squared simplified canopy chlorophyll content index (SCCCI²) for N uptake estimates, and (iii) explore the SCCCI² and similar chlorophyll-sensitive indices for grain quality monitoring. Multispectral images were collected from an unmanned aerial vehicle during both rice-growing seasons. Above-ground biomass and nitrogen (N) uptake were measured at panicle initiation (PI). The performance of single-vegetation-index models in estimating rice N uptake, as previously published, was assessed. Yield and grain quality were determined at harvest. Results showed that canopy reflectance in the visible and near-infrared regions differed between aerobic and ponded rice early in the growing season. Chlorophyll-sensitive indices showed lower values in aerobic rice than in the ponded rice at PI, despite having similar yields at harvest. The SCCCI² model (RMSE = 20.52, Bias = −6.21 Kg N ha⁻¹, and MAPE = 11.95%) outperformed other models assessed. The SCCCI², squared normalized difference red edge index, and chlorophyll green index correlated at PI with the percentage of cracked grain, immature grain, and quality score, suggesting that grain milling quality parameters could be associated with N uptake at PI. This study highlights canopy reflectance differences between high-yielding aerobic (averaging 15 Mg ha⁻¹) and ponded rice at key phenological stages and confirms the validity of a single-vegetation-index model based on the SCCCI² for N uptake estimates in ponded and non-ponded rice crops. Full article

The effects of opening size and braiding angle on the tensile behavior of 3D five-way braided composites were systematically studied, and the mechanical properties, failure modes, and fracture characteristics of the composites were comprehensively analyzed. Initially, a static tensile test was conducted. The results demonstrated that both the tensile strength and tensile modulus of the three-dimensional (3D) braided composites decreased as the braiding angle increased. The sensitivity of the tensile modulus to the aperture size increased significantly as the aperture increased. For specimens with varying braiding angles, smaller apertures were more effective in withstanding higher stress concentrations around the opening, with minimal impact on the tensile strength. In comparison to the laminate composites, the 3D braided composites, regardless of braiding angle, retained higher tensile strength after hole formation at the same aperture size. The fracture of the samples was observed and captured using an optical microscope. It was observed that the failure mode of the 3D braided composites progressively transitioned from fiber fractures to interface debonding with an increase in the braiding angle. After hole formation, stress concentration at the aperture edge caused crack propagation along the braiding direction. Larger apertures resulted in more severe cracks, ultimately leading to specimen failure. Full article

This review focuses on deepening the structural understanding of crack-based strain sensors (CBSS) on stretchable and flexible polymeric substrates and promoting sensor performance optimization. CBSS are cutting-edge devices that purposely incorporate cracks into their functional elements, thereby achieving high sensitivity, wide working ranges, and rapid response times. To optimize the performance of CBSS, systematic research on the structural characteristics of cracks is essential. This review comprehensively analyzes the key factors determining CBSS performance such as the crack mechanism, geometrical factors, and functional structures and proposes optimization strategies grounded in these insights. In addition, we explore the potential of numerical analysis and machine learning to offer novel perspectives for sensor optimization. Following this, we introduce various applications of CBSS. Finally, we discuss the current challenges and future prospects in CBSS research, providing a roadmap for next-generation technologies. Full article

Flexible pipe end fittings (EFs) transfer axial loads by embedding tensile armor within epoxy matrices. The integrity of bonding between the armor and resin profoundly influences the EF load-bearing capacity. This study investigated the debonding failure mechanism at the epoxy-resin–tensile-armor interface in flexible pipe end fittings through integrated experimental and numerical approaches. Combining tensile tests with digital image correlation (DIC) and cohesive zone modeling (CZM), the research quantified the impacts of interfacial defects and adhesive properties on structural integrity. Specimens with varying bond lengths (40–60 mm) and defect diameters (0–4 mm) revealed that defects significantly reduced load-bearing capacity, with larger defects exacerbating strain localization and accelerating failure. A dimensionless parameter, the defect-size-to-bond-length ratio ($\lambda =D/2L$), was proposed to unify defect impact analysis, demonstrating its nonlinear relationship with failure load reduction. High-toughness adhesives, such as Sikaforce^® 7752, mitigated defect sensitivity by redistributing stress concentrations, outperforming brittle alternatives like Araldite^® AV138. DIC captured real-time strain evolution and crack propagation, validating strain concentrations up to 3.2 at defect edges, while CZM simulations achieved high accuracy (errors: 3.0–7.2%) in predicting failure loads. Critical thresholds for λ (λ < 0.025 for negligible impact; λ > 0.05 requiring defect control or high-toughness adhesives) were established, providing actionable guidelines for manufacturing optimization and adhesive selection. By bridging experimental dynamics with predictive modeling, this work advances the design of robust deepwater energy infrastructure through defect management and material innovation, offering practical strategies to enhance structural reliability in critical applications. Full article

Floating wind turbines (FWTs) operate in offshore environments under harsh and varying operating conditions, making frequent in situ monitoring dangerous for maintenance teams and costly for operators. Remote and automated diagnosis, including the stages of detection, identification, and severity characterization of early stage damages in FWTs through advanced vibration-based structural health monitoring (SHM) methods of the machine learning (ML) type, is evidently critical for timely repairs, extending their operational lifecycle, reducing maintenance costs, and enhancing safety. This study investigates, for the first time, the complete (all stages) damage diagnosis problem by employing well-established ML SHM methods and conducting hundreds of experiments on a lab-scale FWT model operating under different wind speeds and directions, both in healthy and damaged states. The latter include two distinct blade cracks of limited length, two added masses attached to the blade edge simulating possible accumulation of ice, and connection degradation at the mounting of the main tower with the floater. The results indicate that the proper training of advanced ML methods using damage-sensitive feature vectors that represent the structural dynamics within the entire frequency bandwidth of measurements may achieve flawless damage diagnosis, reaching $100\%$ success at all diagnosis stages, even when only a minimal number of vibration signals from a limited number of sensors (a single sensor in this study) are used. Full article

Owing to the long-standing statement that natural frequency-based crack detection is not sensitive enough to localised damage to identify small cracks, many natural frequency-based crack detection methods are validated by detecting cracks of moderate size. However, a direct comparison between the crack severity causing a measurable natural frequency change and the crack severity reaching the initiation of crack propagation or leading to brittle fracture is constantly ignored. Without this understanding, it is debatable whether the presented crack detection methods are feasible in practical situations. Through natural frequency calculation and linear elastic fracture mechanics, this study is dedicated to filling the above gap in knowledge. To directly utilize the solution of stress intensity factor, common fracture toughness test specimens featuring a single-edge crack are used. These specimens are essentially cracked rectangular plates under uniform uniaxial tension. Considering the stress resultants obtained via the extended finite element method, the natural frequency of the loaded cracked plates is calculated using the Rayleigh–Ritz method incorporating corner functions. In addition, assuming the specimens as structural components under remote uniform tension, the development of critical load versus crack length is derived based on the solution of the stress intensity factor. Thus, critical crack lengths corresponding to a series of safety factors are obtained by equating service load with critical load. After obtaining natural frequencies of the cracked plates with critical crack lengths, the natural frequency drop caused by a critical crack can be computed. Hence, the critical crack length can be compared with the crack length when the frequency drop is measurable. It is found that the brittleness of the employed metals plays a vital role in the feasibility of natural frequency-based crack detection. Full article

Prolonged rolling contact fatigue between wheels and rails results in the formation of surface cracks on the rail and accurately analyzing the crack expansion behavior is essential to ensuring the safe operation of the train. Drawing upon the principles of fracture mechanics and finite element theory, this study establishes a finite element model of wheel–rail rolling contact that incorporates the presence of cracks. The method utilizes an interaction integral to calculate the stress intensity factors at the leading edge of the crack; then, the Paris formula is used to solve the crack spreading rate. It systematically examines the effects of the initial crack angle, the coefficient of friction of wheels to rails, and crack size on the behavior of fatigue crack propagation. The results indicate that the cracks primarily extend in the depth direction of the rail, transforming the semi-circular surface cracks into elliptical cracks with the major axis oriented along the rail’s width. Crack propagation is primarily driven by model II and III composite crack propagation, with their expansion rates influenced by operating conditions. In contrast, mode-I expansion is less sensitive to these conditions. Under single-variable loading conditions, a smaller initial crack angle results in a faster crack growth rate. Increasing crack length accelerates crack growth, while a higher friction coefficient inhibits it. Full article

Structural components with curved edges are common in many engineering designs. Fatigue cracks, corrosion and other types of defects and mechanical damage often initiate from (or are located close to) edges. Damage and defect detection in the presence of complex geometry represents a significant challenge for non-destructive testing (NDT). To address this challenge, this paper investigates the fundamental mode of the quasi-symmetric edge-guided wave (${QES}_0$) propagating along a curved edge, as well as its scattering characteristics in the presence of different types of edge defects. The finite element (FE) approach is used to investigate the propagation and mode shapes of the ${QES}_0$. It was found that the wave attenuation dramatically increases when the radius-to-thickness ratio is less than 20. Moreover, the mode shapes are significantly affected by the waveguide curvature as well as the excitation frequency. Additionally, to evaluate the sensitivity of ${QES}_0$ to edge defects, different sizes of edge defects were investigated with the FE model, which validated against experimental results. The validated FE model was further employed to quantify the dependence of the amplitude of scattered waves for different types of edge defects. These studies indicate that the amplitude of scattered wave is very sensitive to the presence of edge defects. The main outcome of this work is the demonstrated ability of the ${QES}_0$ wave mode to propagate over long distances and a high sensitivity of this mode to different types of edge defects, which manifest its great potential for detecting and characterising damage near the curved edges of structural components. Full article

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications. Full article

Detecting cracks during structural health monitoring is crucial for ensuring infrastructure safety and longevity. Using drones to obtain crack images and automate processing can improve the efficiency of crack detection. To address the challenges posed by the limited computing resources of edge devices in practical applications, we propose CrackScopeNet, a lightweight segmentation network model that simultaneously considers local and global crack features while being suitable for deployment on drone platforms with limited computational power and memory. This novel network features a multi-scale branch to improve sensitivity to cracks of varying sizes without substantial computational overhead along with a stripe-wise context attention mechanism to enhance the capture of long-range contextual information while mitigating the interference from complex backgrounds. Experimental results on the CrackSeg9k dataset demonstrate that our method leads to a significant improvement in prediction performance, with the highest mean intersection over union (mIoU) scores reaching 82.12%, and maintains a lightweight architecture with only 1.05 M parameters and 1.58 G floating point operations (FLOPs). In addition, the proposed model excels in inference speed on edge devices without a GPU thanks to its low FLOPs. CrackScopeNet contributes to the development of efficient and effective crack segmentation networks suitable for practical structural health monitoring applications using drone platforms. Full article

Corrosion and friction coefficient tests were performed on solid oak wood machined with hard-metal woodworking tools coated with PVD coatings (AlCrN, AlTiN, TiAlN, TiCN and CrN). The tannic acid attacks the carbide more intensively than the PVD coatings. During cutting, corrosion spreads on the cutting edge of the cutter due to mechanical action, which dissolves the cobalt binder of the hard-metal and causes the carbide grains to flake off. After 80 min of contact with the wood, the cobalt content decreases from 3.53 to 1.74%. Depending on the PVD coating material, cracks of 4 to 40 µm in width appear after 120 min (9000 m cutting path). After 120 min of machining, wear, corrosion effects and the influence of corrosion on the coefficients of friction were evaluated for tools with and without PVD coatings. TiCN is the most sensitive to corrosion, while AlCrN and CrN coatings are the least sensitive, with the AlTiN coating being the most affected under real cutting conditions (with mechanical + thermal + corrosion effects) and the tools with CrN and AlCrN coatings being the least affected. Corrosion affects the hard-metal and PVD coatings and reduces the coefficient of friction. The angle between the directions of sliding and sharpening of the cutting edge sharpening significantly influences this parameter. The coefficient of friction of hard-metal WC-Co and PVD coatings is higher in the parallel machining direction than in the perpendicular machining direction and ranges from 16.03% (WC-Co) to 44.8% (AlTiN). The coefficient of friction of hard-metal WC-Co decreases by 5.13% before and after exposure to tannic acid, while the corrosion of PVD coatings reduces it by 4.13% (CrN) to 26.7% (TiAlN). Full article

The corrosion steels phenomenon is one of the main problems in the oil industry, such as in buried transmission pipelines used for high gas pressure for long distances. Steels are protected from the external soil corrosion through a bituminous coating, whose action is coupled with a cathodic protection system, which aims to maintain steel in its protection field and thus to avoid any corrosion risk. However, steels in service may experience external surface defects like corrosion pitting and cracking due to electrochemical or mechanical interactions of bare steel with an aggressive soil solution after steel protection failure. These are concerning phenomena and are the major threats of the pipeline transmission system’s reliability and ecological safety. Corrosion mechanisms are varied and can be evaluated by different methods, such as electrochemical measurements, which are influenced by various factors like temperature, pH, soil characteristics, resistivity, water content, and as well mechanical stresses. Corrosion results from simulated artificial soil solutions showed that steel is sensitive to corrosion by soil. Surface defects detection was carried out using an ultrasonic non-destructive method such as C-Scan Emission testing and the time of flight diffraction technique (TOFD) ultrasonic non-contact testing method. After propagation of the ultrasonic waves, the diffracted ultrasonic reflected wave occurring at the edges of the defects appears due to the presence of a corrosion defect by generating defect echoes. The C-Scan ultrasonic image shows surface reflection, including corrosion defects on interfaces with varying acoustic impedances. The cross-transverse speed ultrasonic propagation through the plate including defect is modified, revealing more surface defects, and cross-transverse speed is shown to increase ultrasonic detection presents some advantages, such as precision and speed of detection without alteration to the structure. This method can be used in the industrial context as an intelligent industrial robotics technique. Full article

The study involved numerical FEA (finite element analysis) of dental implants. Based on this, fatigue tests were conducted according to the PN-EN 14801 standard required for the certification of dental products. Thanks to the research methodology developed by the authors, it was possible to conduct a thorough analysis of the impact of external and internal factors such as material, geometry, loading, and assembly of the dental system on the achieved value of fatigue strength limit in the examined object. For this purpose, FEM studies were based on identifying potential sites of fatigue crack initiation in reference to the results of the test conducted on a real model. The actions described in the study helped in the final evaluation of the dental system design process named by the manufacturer as INTEGRA OPTIMA 3.35. The objective of the research was to identify potential sites for fatigue crack initiation in a selected dental system built on the INTEGRA OPTIMA 3.35 set. The material used in the research was titanium grade 4. A map of reduced von Mises stresses was used to search for potential fatigue crack areas. The research [loading] was conducted on two mutually perpendicular planes positioned in such a way that the edge intersecting the planes coincided with the axis of the system. The research indicated that the connecting screw showed the least sensitivity (stress change) to the change in the loading plane, while the value of preload has a significant impact on the achieved fatigue strength of the system. In contrast, the endosteal implant (root) and the prosthetic connector showed the greatest sensitivity to the change in the loading plane. The method of mounting [securing] the endosteal implant using a holder, despite meeting the standards, may contribute to generating excessive stress concentration in the threaded part. Observation of the prosthetic connector in the Optima 3.35 system, cyclically loaded with a force of F ≈ 300 N in the area of the upper hexagonal peg, revealed a fatigue fracture. The observed change in stress peak in the dental connector for two different force application surfaces shows that the positioning of the dental system (setting of the socket in relation to the force action plane) is significantly decisive in estimating the limited fatigue strength. Full article

Deep, unstable slopes are highly developed in mountainous areas, especially in the Minjiang River Basin, Sichuan Province, China. In this study, to reveal their deformation evolution characteristics for stability evaluation and disaster prevention, multi-period optical remote sensing images (2010–2019), SBAS-InSAR data (January 2018–December 2019), and on-site real-time monitoring (December 2017–September 2020) were utilized to monitor the deformation of a large deep-seated toppling, named the Tizicao (TZC) Toppling. The obtained results by different techniques were cross-validated and synthesized in order to introduce the spatial and temporal characteristics of the toppling. It was found that the displacements on the north side of the toppling are much larger than those on the south side, and the leading edge exhibits a composite damage pattern of “collapse failure” and “bulging cracking”. The development process of the toppling from the formation of a tensile crack at the northern leading edge to the gradual pulling of the rear edge was revealed for a time span of up to ten years. In addition, the correlation between rainfall, earthquakes, and GNSS time series showed that the deformation of the toppling is sensitive to rainfall but does not change under the effect of earthquakes. The surface-displacement-monitoring method in this study can provide a reference for the evolution analysis of unstable slopes with a large span of deformation. Full article

Titanium aluminide alloys have gained attention for their lightweight and high-performance properties, particularly in aerospace and automotive applications. Traditional manufacturing methods such as casting and forging have limitations on part size and complexity, but additive manufacturing (AM), specifically electron beam melting (EBM), has overcome these challenges. However, the surface quality of AM parts is not ideal for sensitive applications, so post-processing techniques such as machining are used to improve it. The combination of AM and machining is seen as a promising solution. However, research on optimizing machining parameters and their impact on surface quality characteristics is lacking. Limited studies exist on additively manufactured TiAl alloys, necessitating further investigation into surface roughness during EBM TiAl machining and its relationship to cutting speed. As-built and heat-treated TiAl samples undergo machining at different feed rates and surface speeds. Profilometer analysis reveals worsened surface roughness in both heat-treated and non-heat-treated specimens at certain machining conditions, with higher speeds exacerbating edge cracks and material pull-outs. The hardness of the machined surfaces remains consistent within the range of 32–33.1 HRC at condition 3C (45 SFM and 0.1 mm/tooth). As-built hardness remains unchanged with increasing spindle and cutting head speeds. Conversely, heat-treated condition 3C surfaces demonstrate greater hardness than condition 1A (15 SFM, and 0.04 mm/tooth), indicating increased hardness with varying feed and surface speeds. This suggests crack formation in the as-built condition is considered to be influenced by factors beyond hardness, such as deformation-related grain refinement/strain hardening, while hardness and the existence of the α₂ phase play a more significant role in heat-treated surfaces. Full article