Search Results (555)

Advanced engineering applications increasingly demand high-performance polymers with exceptional mechanical and thermal properties; however, predicting their processing behavior remains challenging due to complex rheological responses and the lack of integrated experimental–simulation frameworks. This study introduces a novel integrated experimental–computational methodology that combines comprehensive rheological characterization, multi-model fitting, injection molding simulation, and multiphysics finite element analysis (FEA) to investigate the processing capabilities of Polyether Ether Ketone (PEEK) for aircraft bearing applications. Unlike conventional approaches that treat rheological analysis, processing simulation, and structural assessment separately, our framework establishes a coupled material–process–performance relationship through: (i) systematic thermal and mechanical characterization, establishing PEEK’s high melting temperature (343 °C), degradation temperature (575 °C), and tensile strength (95 MPa); (ii) comparative rheological model fitting, demonstrating that the Carreau–Yasuda model accurately predicts non-linear flow behavior with R² = 0.97, outperforming simpler Power Law and Cross models; (iii) CAD-based injection molding simulation, revealing homogeneous flow distribution and optimized pressure profiles; and (iv) thermo-mechanical FEA, coupling thermal expansion with structural stress analysis to evaluate bearing integrity under operational conditions. The key novelty lies in the seamless integration of experimental rheology with multiphysics simulation, validated through rigorous statistical analysis achieving low RMSE (0.6854 MPa for stress, 0.003220 mm for deformation) and high correlation coefficients (R² = 0.97). The results confirm a uniform flow distribution, stable structural performance, and reliable thermo-mechanical response, establishing PEEK’s suitability for high-performance aerospace components. This work contributes a comprehensive, scalable, and transferable framework that bridges experimental analysis and advanced simulation, enabling the predictive optimization of polymer processing parameters and significantly enhancing manufacturing reliability for industrial applications. The findings demonstrate the applicability of the experimental–computational analysis to the investigated PEEK bearing configuration under the specified processing and simulation conditions. Its specific contribution is the application of comparative rheological model fitting and experimentally characterized PEEK properties to the selected bearing geometry and processing conditions. Full article

Electric aviation and unmanned aerial vehicles (UAVs) depend on lightweight composites to preserve payload and range, yet mass reduction, recycled content, or bio-based content alone does not establish component suitability. Candidate systems must also satisfy coupled structural, thermal, fire, electrical, manufacturing, durability, repair, and circularity requirements. This structured critical narrative review evaluates thermoplastic carbon-fibre-reinforced polymer (CFRP) systems, recycled-carbon-fibre composites, natural-fibre systems, bio-based and recyclable matrices, hybrid architectures, and multifunctional composites using a component-specific framework based on source role, evidence maturity, test comparability, and failure consequence. The framework links processing and chemistry to defects, retained performance, repair and recovery, and the evidence required for defined aircraft and UAV components. Thermoplastic CFRP provides the strongest near-term pathway for secondary and semi-structural components, although weld durability, impact tolerance, fire response, and process conformity remain system specific. Recycled-carbon-fibre and natural-fibre systems are most defensible for lower-consequence covers, fairings, housings, interiors, and UAV parts when feedstock variability, moisture, porosity, and fire performance are controlled. Battery enclosures, primary structures, rotor-support members, and structural-battery systems require representative coupled-hazard and component-scale evidence. The resulting adoption pathways are bounded by component and operating conditions, with manufacturing, durability, repair, recovery, and qualification evidence specified for each application. Full article

This study presents the design, theoretical modeling, and experimental validation of a novel mechanical reducer made up of three stages, including two logarithmic spiral gears, offering a continuously variable transmission ratio. Unlike conventional constant transmission ratio reducers (circular gears reducers), this reducer provides high transmission ratios during motion initiation and gradually decreases them per cycle, preventing abrupt torque transition. A comprehensive mechanical model was developed to predict transmission ratio, gear geometry, assistance distance and duration, motor torque, and energy consumption, providing a complete framework for reducer design according to target performance requirements. A proof-of-concept prototype manufactured by additive manufacturing was integrated into a trolley to validate the mechanical model. Experimental results demonstrated good agreement with theoretical predictions, with no difference in distance crossed and 13% difference in assistance duration. The model further predicts substantially lower energy consumption than conventional reducers, while maintaining progressive torque delivery throughout the assistance phase. These results demonstrate the potential of logarithmic spiral gears for variable-ratio mechanical transmissions and provide a predictive framework for the design and optimization of this type of mechanism. This reducer is hypothetically intended for applications requiring progressive torque assistance, such as manual trolleys, wheelchairs, or aircraft. Full article

In aircraft assembly, riveting is a critical joining method that directly determines structural integrity, fatigue life, and overall airframe reliability. With the increasing adoption of human–machine collaborative systems for complex assembly tasks, the rational design of riveting process parameters has become essential for ensuring consistent assembly quality. However, traditional experimental parameter optimization is time-consuming and costly, and lacks generalizability across varying working conditions. To address this challenge, this paper proposes a simulation-based design method for rapidly constructing process parameter schemes in human–machine collaborative aircraft assembly riveting. A theoretical dynamic model of the pneumatic reciprocating riveting gun is established to derive the relationship between input air pressure and piston impact velocity, providing physically grounded loading conditions for numerical simulation. A sequentially coupled numerical simulation method is developed using Ansys LS-DYNA and its Restart function to accurately model the entire multiple reciprocating impact forming process, which incorporating preloading analysis to reflect actual clamping conditions and reset analysis with applied damping to eliminate post-impact oscillations. Taking the riveting assembly of Aluminum (AL) 2024T351 rivets and AL 7039 aluminum sheets as a case study, the simulation successfully reproduces the rivet forming evolution over twelve consecutive impacts, revealing a two-stage deformation mechanism consisting of elastic springback and superimposed elastic-plastic deformation. Experimental verification on a self-built human–machine collaborative riveting platform demonstrates excellent agreement with simulation results in impact counts and upset head height. The proposed method provides a reliable, efficient, and low-cost approach for assembly process parameter calibration, offering direct theoretical support for assembly quality control, process robustness, and reliability assurance in aircraft manufacturing. Full article

Aligned recycled carbon fiber (rCF)/polyetherimide (PEI) tapes are a promising material for lightweight aerospace thermoplastic composite structures with reduced environmental impact, provided that their directional properties can be translated into robust components. To assess the viability of rCF/PEI for aircraft interior applications, this study tracks the development of the “Eco Bracket” across four key stages: sustainability screening, coupon characterization, microstructural interpretation, and application-level structural assessment. A project-level life-cycle assessment compared an Additive Fusion Technology (AFT) rCF/PEI bracket with cast and milled aluminum routes and yielded a short-term climate impact of 0.678 kg CO₂-eq for rCF/PEI, representing a significant reduction compared to 5.1 kg CO₂-eq for cast aluminum and 20.9 kg CO₂-eq for milled aluminum. Coupon testing characterized the anisotropic mechanical response of the rCF/PEI tape, including the elastic moduli and strengths under axial and transverse tension, in-plane shear, and axial and transverse compression. Scanning electron microscopy of a representative fracture surface showed a rough, fiber-dominated morphology with exposed fiber bundles, pull-out, and local variations in matrix coverage, supporting the interpretation that consolidation and local wet-out quality govern the transfer from coupon capability to component performance. At the application level, the manufactured rCF/PEI bracket achieved a peak load of 3517.6 N, corresponding to 93% of the strength of an industry-standard polyetherketoneketone (PEKK) reference bracket, and failed in the fastener-hole region predicted as critical by the finite element model. The combined results indicate that aligned rCF/PEI tapes can deliver mechanical properties consistent with the design requirements of aircraft interior brackets, while process robustness, fiber-path fidelity, and local consolidation quality remain the key barriers to repeatable component performance. Full article

Online flatness detection of aircraft rudder surfaces serves as a pivotal core procedure for ensuring the manufacturing precision, aerodynamic performance and operational safety of aeronautical components. Traditional plane fitting-based detection approaches are constrained by low detection efficiency, susceptibility to local optimal solutions, weak anti-noise robustness and limited automation capability, which fail to satisfy the micron-level high-precision online detection requirements for curved composite rudder surfaces in batch manufacturing scenarios. To address the aforementioned technical bottlenecks, this study proposes a bidirectionally coupled PSO-SA hybrid optimization algorithm for non-convex minimum zone flatness evaluation of curved rudder surfaces, which overcomes the unidirectional open-loop iteration limitation inherent in conventional serial PSO-SA composite frameworks. Two targeted algorithmic improvements are elaborated in this work: a residual-adaptive nonlinear inertia weight strategy, which dynamically balances global exploration and local exploitation capabilities based on the fluctuation characteristics of free-form surface measurement residuals; and a measurement noise-modified Metropolis acceptance criterion, which substantially enhances the algorithm’s anti-interference performance against on-machine trigger sampling noise. Integrating with the trigger-type on-machine detection hardware of computer numerical control (CNC) machine tools, an integrated online detection system is established to realize the full-process functions of point cloud data acquisition, error compensation, intelligent plane fitting and flatness error evaluation. Meanwhile, the complete technical workflow involving measurement path planning, probe calibration and algorithm iterative solution is systematically illustrated. Comparative simulation experiments implemented on the MATLAB platform demonstrate that the proposed algorithm exhibits superior performance in convergence speed, fitting accuracy and optimization stability over five mainstream algorithms, including standard particle swarm optimization (PSO), standard simulated annealing (SA), comprehensive learning PSO (CLPSO), adaptive cooling SA and conventional serial PSO-SA. On-machine physical measurement experiments are conducted on 24 aircraft rudder workpieces covering aluminum alloy skins and assembled riveted components. After multi-dimensional systematic calibration, the overall detection error of the developed system is controlled within 1 μm. The experimental results indicate that the average flatness error calculated by the proposed bidirectionally coupled PSO-SA algorithm is 29.7 μm, which is 30.1% and 38.5% lower than that of standard PSO and standard SA, respectively, fully complying with the aviation flatness tolerance specification of 0.1–0.3 mm. Moreover, the full detection cycle for a single workpiece is only 2.1 min, achieving a 34.4% reduction in detection time compared with standard PSO and effectively improving the efficiency of online in-process inspection. One-way analysis of variance (ANOVA) combined with Tukey’s posthoc test further verifies that the accuracy superiority of the proposed algorithm is statistically significant. This research provides a targeted theoretical basis and complete engineering implementation scheme for intelligent flatness detection of aerospace curved thin-walled parts, and offers a valuable technical reference for form and position error evaluation of irregular industrial components under noisy measurement conditions. Full article

Aircraft wing ribs form the skeletal backbone of the wing. They maintain the aerodynamic profile and transfer structural loads from the skin to the spars. Conventional manufacturing processes struggle to produce complex geometries, making these components difficult and expensive to manufacture. Recent advances in additive manufacturing (AM) address these limitations. Additive manufacturing enables the production of complex geometries that significantly reduce weight. Most designers use the standard Ashby method to identify the strongest or lightest metal. However, they often overlook whether the material will behave as expected during additive printing. This research focuses on Multi-Objective Material Selection for the design of the additive manufacturability of aircraft wing ribs using aluminium-based alloys. A key innovation in this research is the formulation of hybrid performance indices (HPIs). These indices go beyond the traditional Ashby methodology. They mathematically couple structural efficiency metrics with a weighted Processability Factor. The structural metrics include specific density, stiffness, specific strength, and Embodied-Energy-Strength-to-Embodied-Energy Index. The Processability Factor accounts for local material availability, thermal conductivity, printability, recyclability and material cost. This dual evaluation assesses both structural integrity and manufacturing risk simultaneously. The process produces an Additive Pareto Optimal set of candidate materials. This helps engineers predict and prevent issues like warping and residual stress before printing begins. The framework also emphasises sustainability. It prioritises materials that minimise waste and considers embodied energy in the selection process. The framework identifies high-performance aluminium alloys that are specifically optimised for the additive manufacturing of aircraft wing ribs. It provides a definitive ranking based on their ability to withstand aerodynamic loads while remaining easy to print. This data-driven approach replaces trial and error with a clear selection matrix for the early design stage. It ensures that the chosen alloy is both structurally sound and manufacturable for aerospace applications. Full article

Ti-Al intermetallic compounds (Ti-Al IMCs) are emerging as lightweight, high-temperature structural materials with considerable application potential. Owing to their low density and high-temperature capability, these materials can improve the thrust-to-weight ratio of aeroengines, enhance the high-temperature service performance of aircraft, increase fuel efficiency, and improve adaptability to harsh environments. However, their intrinsic room-temperature brittleness leads to high cutting forces, elevated cutting temperatures, and severe tool wear during machining, making it difficult to ensure machining quality and limiting their large-scale applications in the aerospace industry. Ultrasonic vibration-assisted machining (UVAM) introduces a high-frequency, low-amplitude intermittent cutting mechanism that actively regulates material removal and offers a feasible route for overcoming the machining bottleneck of Ti-Al IMCs. This review summarizes the recent progress in UVAM for machining Ti-Al IMCs. First, the typical applications and machining characteristics of Ti-Al IMCs are discussed. Existing studies are then reviewed in terms of cutting performance, including cutting force, cutting temperature, chip morphology, tool wear, and post-machining surface integrity, including surface roughness, surface defects, residual stress, and work hardening. The reviewed evidence indicates that UVAM can reduce cutting forces and temperatures, improve chip morphology, and extend the tool life. It can also improve machined surface integrity by decreasing surface roughness, suppressing surface defects, inducing beneficial residual compressive stress layers, and regulating work-hardening behavior. This review provides systematic theoretical guidance and technical references for improving the machinability of Ti-Al IMCs via UVAM, thereby enabling the controllable, high-performance, and high-reliability fabrication of these difficult-to-machine materials in aerospace precision manufacturing. Full article

The safe and accurate measurement of liquid hydrogen (LH₂) tank fill levels is a critical enabling technology for the adoption of hydrogen as a sustainable aviation fuel. Although LH₂ fill level measurement techniques have been applied in industrial, automotive, and space applications, no system has yet been validated at the scale, robustness, and precision required for modern aircraft Fuel Quantity Indication Systems (FQIS). Differentialpressure sensors are commonly employed in industrial cryogenic systems and hydrogen refueling stations; however, their accuracy is strongly influenced by dynamic effects such as filling transients and liquid sloshing, rendering them unsuitable for aviation-grade FQIS requirements which call for high accuracy and reliability. While simulations and analytical studies propose alternative LH₂ level sensing concepts, experimental validation and direct comparative assessments of different sensor architectures remain scarce. Furthermore, although several manufacturers offer LH₂ fill-level sensors, the stated measurement accuracies have not been independently verified, highlighting the need for systematic experimental investigation under representative operating conditions. A complete evaluation of an ${\mathrm{LH}}_2$ FQIS requires testing under anticipated flight conditions, including accelerations, varying attitudes, vibrations, dynamic sloshing, and long-term cycling. As a preliminary investigation, this work experimentally evaluates five liquid level sensing concepts based on measurements of dielectric constant, thermal capacity, and optical absorption properties using liquid nitrogen (${\mathrm{LN}}_2$) as a representative surrogate for ${\mathrm{LH}}_2$ under quasi-static conditions. The results demonstrate that optical absorption-based sensors in the near-infrared spectrum are unsuitable for LH₂ and LN₂ liquid level measurement. In contrast, capacitive probes and resistive thermal devices (RTDs) exhibit robust and repeatable performance under cryogenic conditions, demonstrating measurement resolutions of better than $5.1\mathrm{mm}$. These findings provide experimentally grounded guidance for the development of future LH₂-compatible FQIS architectures for aviation applications. Full article

Lightweight composite aircraft wing design increasingly depends on combining multidisciplinary design optimization (MDO), aeroelastic tailoring, and stacking sequence optimization. However, an overview of these interconnected fields is lacking. This study applies a PRISMA-ScR-based scoping review of 54 selected articles to map current approaches, identify emerging trends, and highlight remaining gaps. Key findings indicate six MDO architectures—with hybrid methods being increasingly preferred—and demonstrate that aeroelastic tailoring (e.g., ply angle manipulation) enhances performance while reducing weight. Manufacturing constraints (ply continuity, blending, symmetry) are addressed in a subset of the reviewed literature, with opportunities for broader integration. Critical future priorities include integrating manufacturing process models into MDO and incorporating durability considerations (fatigue, impact). This work synthesizes current approaches, identifies emerging trends, and provides a roadmap for the development of next-generation lightweight, high-performance composite wings. Full article

This paper addresses a human resource allocation problem with spatial-temporal constraints (HRAP-SC) in the parallel assembly of complex products, such as satellites and aircraft. It involves coordinating a limited pool of multi-skilled workers across geographically distributed workstations, subject to rigorous constraints including team collaboration requirements, operation priorities, technological tail times (e.g., curing), and strict 8 h workdays. Existing exact approaches typically fail to converge due to the combinatorial explosion arising from the strong coupling of shared resources across workstations, while meta-heuristic methods often suffer from performance instability caused by hyper-parameter sensitivity. To overcome these limitations, we propose a pattern-based decomposition algorithm (PDA), a novel parameter-free exact solution framework. By exploiting the inherent symmetry of identical jobs and parallel workstations, PDA defines a set of canonical patterns to drastically reduce the search space. It employs an efficient traversal mechanism reinforced by rigorous mathematical bounds and pruning rules to eliminate unpromising solutions. Computational experiments demonstrate that PDA significantly outperforms state-of-the-art Mixed-Integer Programming (MIP) and Constraint Programming (CP) solvers. Unlike standard solvers, which frequently time out (3600 s), PDA strictly evaluates only a single pattern when proving optimality, and robustly scales to large industrial instances (e.g., six jobs comprising 78 operations) to provide high-quality schedules. By successfully solving complex scheduling problems that remain intractable for monolithic solvers, PDA provides a robust and automated decision-support tool for production management in complex manufacturing systems. Full article

Graphene is a novel material that can bring several advantages in the composite materials manufacturing field, such as improved electrical and thermal properties, and high performance. In particular, functionalizing current composite materials can bring advantages in the aerospace field in thermal management for electric aircraft engines. This paper studies the addition of graphene particles into carbon fiber composites manufactured by the Resin Transfer Molding Process (RTM). Thermal and mechanical properties are evaluated and compared with a conventional composite laminate. Major improvements were achieved on the thermal behavior of the composite material while maintaining general properties, but in particular, the addition of graphene had a negative impact on transverse tensile and mode II fracture toughness due to agglomerates present in the fiber–resin interface. Full article

This paper introduces a refined criterion for evaluating and optimizing the aerodynamic efficiency of compound planform wings, specifically those whose half span is formed by multiple trapezoidal segments. While elliptical lift distribution is known to minimize induced drag, practical manufacturing constraints have led to widespread adoption of tapered wings. However, conventional single-trapezoid planforms deviate significantly from the ideal elliptical distribution, resulting in increased induced drag and reduced fuel efficiency. This study proposes an adjustment to the ellipticity factor, enabling quantitative assessment of how well a multi-trapezoid wing approximates elliptical chord distribution. The methodology is validated through analysis of existing transport aircraft, identifying configurations with ellipticity factors below 5% (e.g., Lockheed C-5A, Antonov An-124) that achieve near-optimal induced drag performance. A comparative case study of a virtual 40-ton aircraft with a 100 m² wing area quantifies trade-offs between three planform configurations. Computational fluid dynamics simulations confirm that increasing trapezoidal segmentation improves spanwise loading and delays flow separation. Results demonstrate that two-trapezoid configurations with total inverse taper ratios of 3.3–4.2 and break coordinates at 35–45% half span achieve ellipticity factors under 7%, offering an optimal balance between aerodynamic efficiency, structural feasibility, and tail surface requirements. The proposed criterion provides aircraft designers with a rapid, computationally efficient tool for planform optimization at the conceptual design stage. The proposed criterion is valid for subsonic cruise conditions (M ≤ 0.85) and does not account for wave drag or aeroelastic effects. Full article

This paper examines the transformation of traditional aircraft maintenance into an AI-driven, digitized process through the evolution of the Onboard Maintenance System (OMS). It conceptualizes the OMS as an “airborne e-hangar,” where embedded artificial intelligence functions operate as virtual engineering teams performing continuous monitoring, diagnostics, and predictive maintenance during flight. Using the literature review synthesis of aviation regulations, technical manuals, and industry practices, the study outlines how OMS integrates subsystems, such as condition monitoring, central maintenance, and electronic logbooks, to enable real-time data processing and fault isolation. Findings highlight that AI-enhanced OMS improves maintenance efficiency, reduces human error, and supports proactive decision-making by converting operational data into actionable insights. The system facilitates seamless data exchange between aircraft and ground operations, enhancing troubleshooting, maintenance planning, and airworthiness compliance. Furthermore, the continuous feedback loop among manufacturers, maintenance organizations, and regulatory authorities contributes to improved aircraft reliability and design optimization. The study underscores the role of AI in minimizing downtime, optimizing maintenance schedules, and enhancing flight safety while maintaining human oversight through advanced interfaces. The originality lies in framing OMS as a fully digitized, intelligent maintenance ecosystem that redefines aircraft maintenance practices and supports safer, more efficient aviation operations. Full article

Birds actively modulate their wing and tail morphologies to achieve high aerodynamic efficiency and maneuverability, enabling long-duration gliding while retaining the ability to execute rapid maneuvers. Innovations in aircraft design and control are increasingly inspired by these avian flight characteristics through control surfaces that imitate the natural wing and tail movements of birds. This paper presents a non-flapping, unmanned aerial vehicle (UAV), called “SMART Hawk” (Shape-Morphing Artificial Red-Tailed Hawk), inspired by the flight and physical characteristics of Buteo jamaicensis, known as the Red-Tailed Hawk (RTH), which exhibits excellent soaring abilities and agility characteristic of birds of prey. To determine the design parameters required for flight, a mathematical model was developed in MachUpX, then validated and refined using Reynolds-averaged computational fluid dynamics (CFD) models in ANSYS Fluent. SMART Hawk incorporates biomimetic wing and tail morphing, including coordinated forward sweep of the mid-wing and aft sweep of the outer wing, as well as active tail pitch, roll, and feather tucking and expansion. The drone was manufactured from a combination of composite, wood, and 3D-printed components. Multiple flight tests were conducted with proof-of-concept prototypes to demonstrate the design’s effectiveness. Full article