Search Results (191)

Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and fatigue cracks. From this standpoint, the present study combined a 1-D convolutional neural network (1-D CNN) with a long short-term memory (LSTM) algorithm for classifying different ball-bearing health conditions. A physics-guided method that adopts fault characteristics frequencies was used to calculate an optimal input size (sample length). Moreover, grid search was utilized to optimize (1) the number of epochs, (2) batch size, and (3) dropout ratio and further enhance the efficacy of the proposed 1-D CNN-LSTM network. Therefore, an attempt was made to reduce epistemic uncertainty that arises due to not knowing the best possible hyper-parameter configuration. Ultimately, the effectiveness of the physics-guided optimized 1-D CNN-LSTM was tested by comparing its performance with other state-of-the-art models. The findings revealed that the average accuracies could be enhanced by up to 20.717% with the help of the proposed approach after testing it on two benchmark datasets. Full article

Corrosion, combined with cyclic loading, is inevitable and becomes a challenging problem, even when inherently corrosion-protected materials have been selected and applied based on established in-house experience. Aero engine mount structures are exposed to dusty and salty environmental conditions during both operational and non-operational periods. It is becoming tough to predict the remaining useful corrosion fatigue life due to the unascertainable material strength degradations under service conditions. As such, a rationalized approach is currently being used to assess their structural integrity, which produces more wastages of the flying parts. This paper presents a novel approach for predicting corrosion fatigue by proposing a random-parameter model in combination with validated experimental data. The two-random-parameter model is employed here with the probability method to determine the time-independent corrosion fatigue life of a magnesium structural casting, which is used heavily in engine front-mount aircraft systems. This is also correlated with experimental data from the literature, validating the proposed stochastic corrosion fatigue model that addresses the technical variances that occur during service to increase optimal mount structure usage using an automated data system. Full article

In today’s aviation industry, research and studies are carried out to manufacture and design lightweight, high-performance materials. One of the materials developed in line with this goal is glass laminate aluminum-reinforced epoxy (GLARE), which consists of thin aluminum sheets and S2-glass/epoxy layers. Because of its high impact resistance and excellent fatigue and damage tolerance properties, GLARE is used in different aircraft parts, such as the wing, fuselage, empennage skins, and cargo floors. In this study, a survey was carried out and a low-velocity impact model for GLARE materials was developed using the ABAQUS (2014) version V6.14 software and compared with the results of low-velocity impact tests performed according to the American Society for Testing and Materials (ASTM) D7136 standard. This article introduces a novel integrated approach that combines detailed numerical modeling with experimental validation of GLARE 4A FMLs under low-velocity impact. Leveraging ABAQUS, a robust FEM featuring explicit analysis, cohesive resin interfaces, and custom VUMAT subroutines was developed to accurately simulate energy absorption, dent depth, and delamination. The precise model’s predictions align well with test results performed according to ASTM D7136 standards, exhibiting less than a 0.1% deviation in the displacement (dent depth)–time response, along with deviations of 4.3% in impact energy–time and 5.2% in velocity–time trends at 5.5 ms. Full article

With the increasing demand for drag reduction, energy consumption reduction, and low weight in civil aircraft, high-precision microgroove preparation technology is being developed internationally to reduce wall friction resistance and save energy. Compared to mechanical processing, chemical etching, roll forming, and ultrafast laser processing, nanosecond lasers offer processing precision, high efficiency, and controllable thermal effects, enabling low-cost and high-quality preparation of microgrooves. However, the impact of nanosecond laser etching on the fatigue performance of substrate materials remains unclear, leading to controversy over whether high-precision shape control and fatigue performance enhancement in microgrooves can be achieved simultaneously. This has become a bottleneck issue that urgently needs to be addressed. This paper focuses on the current research status of nanosecond laser processing quality control for microgrooves and the research status of laser effects on enhancing the fatigue performance of substrate materials. It identifies the main existing issues: (1) how to induce surface residual compressive stress through the thermo-mechanical coupling effect of nanosecond lasers to suppress micro-defects while ensuring high-precision shape control of fixed microgrooves; and (2) how to quantify the regulation of nanosecond laser process parameters on residual stress distribution and fatigue performance in the microgroove area. To address these issues, this paper proposes a collaborative strategy for high-quality shape control and surface strengthening in fixed microgrooves, an analysis of multi-dimensional fatigue regulation mechanisms, and a new method for multi-objective process optimization. The aim is to control the geometric accuracy error of the prepared surface microgrooves within 5% and to enhance the fatigue life of the substrate by more than 20%, breaking through the technical bottleneck of separating “drag reduction design” from “fatigue resistance manufacturing”, and providing theoretical support for the integrated manufacturing of “drag reduction-fatigue resistance” in aircraft skins. Full article

Aircraft mechanical maintenance involves high loads, repetitive movements, and awkward postures, significantly increasing the risk of work-related musculoskeletal disorders (WMSDs). Traditional static evaluation methods based on posture analysis fail to capture the complexity and dynamic nature of these tasks, limiting their applicability in maintenance settings. To address this limitation, this study introduces a novel quantitative WMSD risk assessment model that leverages 3D motion data collected through an optical motion capture system. The model evaluates dynamic human postures and employs an inverse trigonometric function algorithm to quantify the loading effects on working joints. Experimental validation was conducted through quasi-real-life scenarios to ensure the model’s reliability and applicability. The findings demonstrate that the proposed methodology provides both innovative and practical advantages, overcoming the constraints of conventional assessment techniques. Specifically, it enables precise quantification of physical task loads and enhances occupational injury risk assessments. The model is particularly valuable in physically demanding industries, such as aircraft maintenance, where accurate workload and fatigue monitoring are essential. By facilitating real-time ergonomic analysis, this approach allows managers to monitor worker health, optimize task schedules, and mitigate excessive fatigue and injury risks, ultimately improving both efficiency and workplace safety. Full article

In recent decades, the application of composite materials in aerostructures has significantly increased, with modern commercial aircraft progressively replacing aluminum alloys with composite components. This shift is exemplified by comparing the material compositions of the Boeing 777 and the Boeing 787 (Dreamliner). The Boeing 777 incorporates approximately 50% aluminum alloy and 12% composite materials, whereas the Dreamliner reverses this ratio, utilizing around 50% composites and 12% aluminum alloy. While metals remain advantageous due to their availability and ease of machining, composites offer greater potential for property tailoring to meet specific performance requirements. They also provide superior strength-to-weight ratios and enhanced resistance to corrosion and fatigue. To ensure the reliability of composites in aerospace applications, comprehensive testing under various loading conditions, particularly impact, is essential. Impacts were performed on quasi-isotropic (QIT) carbon-fiber reinforced epoxy panels with stainless steel, round-nosed and flat-ended impactors with rubber discs of 1-, 1.5- and 2 mm thickness, adhered to the flat-ended impactor to simulate the transition between hard and soft impact loading conditions. QIT composite panels were tested in this research employing similar lay-ups often being implemented in aircraft wings and other structures. The rubber discs were applied in the flat-ended impactor case but not for the round-nosed impactor due to the limited adhesion between the rubber and the rounded stainless-steel surface. Impact energies of 7.5, 15 and 30 J were investigated, and the performance of the panels was evaluated using force-time and force-displacement data alongside post-impact ultrasonic C-scan imaging to assess the damaged area. Damage was observed at all three energy values for the round-nosed impacts but only at the highest impact energy when using the flat-ended impactor, leading to the hardness study with adhered rubber discs being performed at 30 J. The most noticeable difference with the addition of rubber discs was the reduction in the damage in the plies nearest the top (impacted) surface. This suggests that the rubber reduces the severity of the impact, but increasing the thickness of the rubber from 1 to 2 mm does not notably increase this effect. Indentation clearly plays a significant role in promoting delamination at low-impact energies for the round-nosed impactors. Full article

According to high-precision damage identification of aircraft complex configuration fatigue key structures, a high-precision mathematical model of complex configuration structure is established with the use of digital twin technology to realize the real-time and accurate characterization of a physical entity from the macro to the micro state. Meanwhile, the damage identification information obtained by different sensor technologies and systems is used to deduce the exact state of the damage or crack. In other words, the advantage of a multi-source data drive is used to improve the effectiveness of the overall monitoring system and eliminate the limitations of single-sensor monitoring technology. Each sensor system directly transmits their filtered data information to the fusion center (digital twin system). There is no influence between each sensor; the digital twin carries out comprehensive processing and fusion analysis of each piece of information in an appropriate manner, according to the built-in algorithm and mechanism, and then outputs the final damage identification and fault diagnosis results. Full article

In this paper, we introduce an advanced AI-based solution for predicting structural damage in aircraft laminates. Our innovative approach focuses on detecting and locating fatigue damage within composite structures, thereby enhancing the assessment of aircraft health and usage. By leveraging state-of-the-art ultrasonic guided wave (UGW) inspection simulations of composite laminates integrated with piezoelectric transducers, comprehensive datasets are extracted efficiently. The signals captured by the piezoelectric sensors are utilized to engineer key features sensitive to composite structural damage, which are then used to train a deep neural network (DNN) for accurate structural damage prediction. Our findings demonstrate the significant potential of combining advanced simulation techniques with machine learning to improve the accuracy and reliability of structural health monitoring in aerospace applications. Full article

Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to $1\times {10}^{10}$ cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20–30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications. Full article

The aircraft landing gear system is vital in ensuring the aircraft’s functional completeness and operational safety. The mechanical structures of the landing gear must withstand significant operational forces, including repeated high-intensity impact loads, throughout their service life. At the same time, they must resist environmental degradation, such as corrosion, temperature fluctuations, and humidity, to ensure structural integrity and long-term reliability. Under this premise, investigating material-related mechanical failures in the landing gear is of great significance for preventing landing gear failures and ensuring aviation safety. Compared to failure investigations, structural health monitoring (SHM) plays a more active role in failure prevention for aircraft landing gears. SHM technologies identify the precursors of potential failures and continuously monitor the operational or health conditions of landing gear structures, which facilitates condition-based maintenance. This paper reviews various landing gear material-related failure investigations. The review suggests a significant portion of these failures can be attributed to material fatigue, which is either induced by abnormal high-stress concentration or corrosion. This paper also reviews a series of load monitoring-based landing gear SHM studies. It is revealed that weight and balance measurement, hard landing detection, and structure load monitoring are the most typical monitoring activities in landing gears. An analytical discussion is also presented on the correlation between reviewed landing gear failures and SHM activities, a comparison of sensors, and the potential shift in load-based landing gear SHM in response to the transition of landing gear design philosophy from safe life to damage tolerance. Full article

The integration of distributed electric propulsion into a wing significantly alters the dynamic behavior of the wing. Consequently, the loads on the wing structure in service, in particular upon transient gust and landing impact loads, change substantially compared with conventional aircrafts with main engines mounted on the inner wing. As this might significantly increase the stress levels and number of load cycles, this paper assesses the impact of wing-integrated distributed propulsion on the fatigue behavior of the wing structure. This assessment is conducted based on a retrofit scenario of a conventional 19-seater commuter aircraft of the CS-23 category retrofitted with distributed electric propulsion. The wing structure is idealized with beam elements. Static and dynamic response analyses followed by stress analyses are conducted for typical load cases occurring during operation of the aircraft. The fatigue analysis is carried out based on the safe life approach. This study concludes that the integration of distributed electric propulsion has a substantial impact on the fatigue behavior of the wing. A significant increase in fatigue damage for the electric configurations compared with the conventional configuration is observed, in particular in the outer wing area. The increased damage accumulation is a result of the higher stress amplitudes and the longer decay duration of the structural vibrations due to gusts. The results suggest that adjustments to the structural design and maintenance procedures of future electric aircrafts may be necessary. Full article

Cylindrical shells are widely used in pipelines, pressure vessels, and aircraft fuselages due to their efficient internal pressure distribution. However, axial cracks caused by fatigue, environmental effects, or mechanical loading compromise structural integrity, requiring effective reinforcement. This study presents a finite element modeling approach integrating p-refinement techniques for the efficient analysis of axially cracked pipes reinforced with composite patches. The proposed method unifies equivalent single-layer and layer-wise theories into a single finite element type, improving computational efficiency and eliminating the need for multiple element types in transition elements. Benchmark studies show that the proposed model accurately predicts mechanical behavior, with maximum displacement and stress intensity factors (SIFs) deviating by less than 5% from reference solutions. Fracture analysis using the virtual crack closure technique confirms the accuracy of the SIF calculations. In patched cracked pipes, the proposed model achieves a 67% reduction in degrees of freedom compared to conventional p-refinement layer-wise models, while maintaining computational accuracy. Additionally, boron–epoxy composite patches reduce SIFs by up to 40%, demonstrating effective crack reinforcement. These findings support computationally efficient damage-tolerant design strategies for pressurized cylindrical structures in aerospace, marine, and mechanical engineering. Full article

Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of military training aircraft, which face demanding conditions such as high maneuverability, variable loads, and extreme environments, leading to structural fatigue. Traditional methods, such as modal analysis, often struggle to handle the multivariate complexity of operational conditions and data variability. Recently, deep learning has emerged as a promising alternative to overcome these limitations. However, deep learning models typically operate in a unidirectional manner, where feedback to the inputs is often neglected. In contrast, biological neurons utilize feedback mechanisms to refine and adapt their responses in natural ecosystems, enabling adaptive learning and error correction. In this context, this study proposes an innovative Convolutional Neural Network with Reversed Mapping (CNN-RM) approach to SHM, which incorporates feedback loops and self-correcting mechanisms. Before feeding the data into CNN-RM, the dataset complexity is reduced through time-series-to-images Continuous Wavelet Transform (CWT), followed by a denoising CNN (DnCNN) to mitigate complex behavior under various conditions. For application, this study utilizes a massive dataset collected from multivariate sensors installed on a decommissioned military training aircraft previously used by the British Royal Air Force and now housed in a laboratory environment. The results revealed that the overall mean of classification metrics for the CNN is 0.9673 (training) and 0.9422 (testing), while for CNN-MR, it is 0.9764 (training) and 0.9515 (testing), showing an improvement of 0.94% in training and 1.00% in testing. These results highlight significant advancements in SHM, recommending the consideration of such learning mechanisms in neural learning models. Full article

Residual stress (RS) in laser powder bed fusion (LPBF) additive manufactured structures can significantly affect mechanical performance, potentially leading to premature failure. The complex distribution of residual stresses, combined with the limitations of full-field measurement techniques, presents a substantial challenge in conducting damage tolerance analyses of aircraft structures. To address these challenges, this study developed a comprehensive simulation framework to analyze the 3D distribution of residual stresses and fatigue crack growth in LPBF parts. The 3D residual stress profiles of as-built samples in 15° and 75° build directions were computed and compared to experimental data. The fatigue crack propagation behavior of the 75° sample, considering 3D residual stress, was predicted, and the effects of residual stress redistribution under cyclic loading were discussed. It shows that the anisotropy of residual stress, influenced by the build direction, can lead to mixed-mode fracture and subsequent crack deflection. Tensile residual stress in the near-surface region and compressive stress in the inner region can cause an inverted elliptical crack front and accelerate fatigue crack growth. Full article

Due to the complexity of the landing gear’s (LG) structural integrity and its loads under various static or dynamic working conditions, the fatigue life assessment for LG is a highly challenging task. On the basis of the whole geometric model of a large passenger aircraft’s main landing gear (MLG), the quasi-static finite element model (FEM) of the whole MLG is established, and the high-cycle fatigue issue of the Main Fitting (MF) is studied by considering the variation in shock absorber travel (SAT). Firstly, the ground loads under actual fatigue conditions are equivalently converted into the forces acting on the center of the left and right axles of the MLG, and based on these spatial force decompositions, the magnitude and direction of the load for 12 different basic unit load cases (ULC) are obtained. That is, the stress of the MLG under actual fatigue conditions can be obtained by superimposing these ULCs. Then, considering that the SAT of the MLG varies under different fatigue conditions, and to reduce the number of finite element (FE) simulations, this article simplifies all the SAT experienced by the MLG into seven specific values, so as to establish seven quasi-static FEMs of the MLG with the specified stroke of the shock absorber. In this way, the fatigue stress of the MLG with any actual SAT can be obtained by interpolating the stress components of the seven FEMs. Only 84 FE simulations are needed to efficiently obtain the fatigue stress spectra from the ground load spectra. Finally, according to the material S-N curve and Miner’s damage accumulation criterion, evaluate the fatigue life of the Main Fitting. The results of the stress component interpolation and superposition method show that at least five different SATs of the whole MLG’s FEM are needed to effectively convert the fatigue loads into a stress spectrum. The fatigue life prediction results indicate that the minimum lifespan of the MF is 53164 landings, which means that the fatigue life meets the requirement design. Full article