Search Results (2,006)

This study systematically investigates the high-velocity impact response and energy absorption characteristics of carbon fiber-reinforced plastic (CFRP)—aluminum foam (AlF) hybrid composite structures, aiming to address the growing demand for lightweight yet high-performance energy-absorbing materials in aerospace and protective engineering applications. Particular emphasis is placed on elucidating the influence of key geometric and material parameters, including the aspect ratio of the columns and the relative density of the AlF core. Experimental characterization was first performed using a split Hopkinson pressure bar (SHPB) apparatus to evaluate the dynamic compressive behavior of AlF specimens with four different relative densities (i.e., 0.163, 0.245, 0.374, and 0.437). A finite element (FE) model was then developed and rigorously validated against the experimental data, demonstrating excellent agreement in terms of deformation modes and force–displacement responses. Extensive parametric studies based on the validated FE framework revealed that the proposed CFRP-AlF composite structure achieves a balance between specific energy absorption (SEA) and peak crushing force, showing a significant improvement over conventional CFRP or AlF. The confinement effect of CFRP enables AlF to undergo progressive collapse along designated orientations, thereby endowing the CFRP-AlF composite structure with superior impact resistance. These findings provide critical insight for the design of next-generation lightweight protective structures subjected to extreme dynamic loading conditions. Full article

Polyetheretherketone (PEEK) is a semi-crystalline thermoplastic polymer which, due to its very high mechanical properties and high chemical resistance, has found application in the automotive, aerospace, chemical, food and medical (biomedical engineering) industries. Owing to the use of additive technologies, particularly the Fused Filament Fabrication (FFF) method, this material is the most widely used plastic to produce skull reconstruction implants, parts of dental implants and orthopedic implants, including spinal, knee and hip implants. PEEK enables the creation of personalized implants, which not only have greater elasticity compared to implants made of metal alloys but also resemble the physical properties of the cortical layer of human bone in terms of their mechanical properties. Therefore, the aim of this article is to characterize polyether ether ketone as an alternative material used in the manufacturing of implants in orthopedics and dentistry. Full article

A method for selecting mechanical properties and geometry of reinforcing overlays to increase the strength of composite structural elements with holes has been developed. The method is based on the developed algorithm for calculating stress concentration near holes reinforced with inserted rings or glued composite reinforcing overlays. The determination of stresses near holes and overlays is reduced to solving a system of singular integral equations. The kernels of these equations are constructed using Green’s solution, which allows a reduction in the number of equations to four. It is shown that the stress concentration near holes can be significantly reduced by optimizing the thickness, elastic properties, and shape of the overlays. The stress calculations performed based on the three-dimensional theory of elasticity confirmed the reliability of the results obtained within the framework of the plane problem of an anisotropic body. The results obtained, in accordance with the concept of sustainable development, enable the develop simple methods for increasing reliability, reducing material consumption, and reducing the manufacturing and operating costs of composite structures in the aerospace and mechanical engineering industries. Full article

Al-Mg-Si (6XXX) series aluminum alloys are widely applied in aerospace and transportation industries. However, exploring how varying compositions affect alloy properties and deformation mechanisms is often time-consuming and labor-intensive due to the complexity of the multicomponent composition space and the diversity of processing and heat treatments. This study, inspired by the Materials Genome Initiative, employs high-throughput experimentation—specifically the kinetic diffusion multiple (KDM) method—to systematically investigate how the pop-in effect, indentation size effect (ISE), and creep behavior vary with the composition of Al-Mg-Si alloys at room temperature. To this end, a 6016/Al-3Si/Al-1.2Mg/Al KDM material was designed and fabricated. After diffusion annealing at 530 °C for 72 h, two junction areas were formed with compositional and microstructural gradients extending over more than one thousand micrometers. Subsequent solution treatment (530 °C for 30 min) and artificial aging (185 °C for 20 min) were applied to simulate industrial processing conditions. Comprehensive characterization using electron probe microanalysis (EPMA), nanoindentation with continuous stiffness measurement (CSM), and nanoindentation creep tests across these gradient regions revealed key insights. The results show that increasing Mg and Si content progressively suppresses the pop-in effect. When the alloy composition exceeds 1.0 wt.%, the pop-in events are nearly eliminated due to strong interactions between solute atoms and mobile dislocations. In addition, adjustments in the ISE enabled rapid evaluation of the strengthening contributions from Mg and Si in the microscale compositional array, demonstrating that the optimum strengthening occurs when the Mg-to-Si atomic ratio is approximately 1 under a fixed total alloy content. Furthermore, analysis of the creep stress exponent and activation volume indicated that dislocation motion is the dominant creep mechanism. Overall, this enhanced KDM method proves to be an effective conceptual tool for accelerating the study of composition–deformation relationships in Al-Mg-Si alloys. Full article

In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R² test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage. Full article

Carbon fiber (CF) and carbon fiber-reinforced polymers (CFRPs) are widely used in the aerospace, automotive, and energy sectors due to their high strength, stiffness, and low density. However, significant waste is generated during manufacturing and after the use of CFRPs. Traditional disposal methods like landfilling and incineration are unsustainable. CFRP machining processes, such as drilling and milling, produce fine chips and dust that are difficult to recycle due to their heterogeneity and contamination. This study investigates the oxidation behavior of CFRP drilling waste from two types of materials (tube and plate) under oxidative (non-inert) conditions. Thermogravimetric analysis (TGA) was performed from 200 °C to 800 °C to assess weight loss related to polymer degradation and carbon fiber integrity. Scanning electron microscopy (SEM) was used to analyze morphological changes and fiber damage. The optimal range for removing the polymer matrix without significant fiber degradation has been identified as 500–600 °C. At temperatures above 700 °C, notable surface and internal fiber damage occurred, along with nanostructure formation, which may pose health and environmental risks. The results show that partial fiber recovery is possible under ambient conditions, and this must be considered regarding the harmful risks to the human body if submicron particles are inhaled. This research supports sustainable CFRP recycling and fire hazard mitigation. Full article

Magnetic gears (MGs) are attracting increasing attention in power transmission systems due to their contactless operation principles, low frictional losses, and high efficiency. However, the broad application potential of these technologies requires a comprehensive evaluation of engineering parameters, such as material selection, energy efficiency, and structural design. This review focuses solely on solid-core magnetic gear systems designed using laminated electrical steels, soft magnetic composites (SMCs), and high-saturation alloys. This review systematically examines the topological diversity, torque transmission principles, and the impact of various core materials, such as electrical steels, soft magnetic composites (SMCs), and cobalt-based alloys, on the performance of magnetic gear systems. Literature-based comparative analyses are structured around topological classifications, evaluation of material properties, and performance analyses based on losses. Additionally, the study highlights that aligning material properties with appropriate manufacturing methods, such as powder metallurgy, wire electrical discharge machining (EDM), and precision casting, is essential for the practical scalability of magnetic gear systems. The findings reveal that coaxial magnetic gears (CMGs) offer a favorable balance between high torque density and compactness, while soft magnetic composites provide significant advantages in loss reduction, particularly at high frequencies. Additionally, application trends in fields such as renewable energy, electric vehicles (EVs), aerospace, and robotics are highlighted. Full article

A novel stainless steel with high Mn and Mo content (much higher than traditional stainless steel), designated CH241SS, was developed as a potential replacement for Cr-Mo-V alloy steel in the cold forging applications of precision industry. Through carbon reduction in an environmentally friendly manner and a two-stage heat treatment process, the hardness of as-cast CH241 was tailored from HRC 37 to HRC 29, thereby meeting the industrial specifications of cold-forged steel (≤HRC 30). X-ray diffraction analysis of the as-cast microstructure revealed the presence of a small amount of ferrite, martensite, austenite, and alloy carbides. After heat treatment, CH241 exhibited a dual-phase microstructure consisting of ferrite and martensite with dispersed Cr(Ni-Mo) alloy carbides. The CH241 alloy demonstrated excellent high-temperature stability. No noticeable softening occurred after 72 h for the second-stage heat treatment. Based on the mechanical and room-temperature tensile fatigue properties of CH241-F (forging material) and CH241-ST (soft-tough heat treatment), it was demonstrated that the CH241 stainless steel was superior to the traditional stainless steel 4xx in terms of strength and fatigue life. Therefore, CH241 stainless steel can be introduced into cold forging and can be used in precision fatigue application. The relevant data include composition design and heat treatment properties. This study is an important milestone in assisting the upgrading of the vehicle and aerospace industries. Full article

Helical milling presents a promising alternative to conventional drilling for hole production, offering superior surface quality and improved production efficiency. While this technique has been extensively applied in the aerospace industry, its potential for machining common engineering materials, such as AISI 1045 steel, remains underexplored in the literature. This study addresses this gap by systematically evaluating the influence of key process parameters—cutting speed (V_c), axial depth of cut (a_p), and tool diameter (D_t)—on hole quality attributes, including surface roughness, burr formation, and nominal diameter accuracy. A full factorial experimental design (2³) was employed, coupled with analysis of variance (ANOVA), to quantify the effects and interactions of these parameters. The results reveal that, with a higher V_c, it is possible to reduce surface roughness (R_a) by 30% to 40%, while an increased a_p leads to a 50% increase in R_a. Additionally, D_t emerged as the most critical factor for nominal diameter accuracy, reducing geometrical errors by 1% with a larger D_t. Burr formation was predominantly observed at the lower end of the hole, highlighting challenges specific to this technique. These findings provide valuable insights into optimizing helical milling for low-carbon steels, offering a foundation for broader industrial adoption and further research. Full article

The increasing utilization of hard-to-cut materials in high-performance sectors such as aerospace and defense has pushed manufacturing systems to be flexible in processing large workpieces with a wide range of materials while also delivering high precision. Recent studies have highlighted the potential of integrating industrial robots (IRs) with electric discharge machining (EDM) to create a non-contact, low-force manufacturing platform, particularly suited for the accurate machining of hard-to-cut materials into complex and large-scale monolithic components. In response to this potential, a novel robotic EDM system has been developed. However, the manual programming and control of such a convoluted system present a significant challenge, often leading to inefficiencies and increased error rates, creating a scenario where the EDM process becomes unfeasible. To enhance the industrial applicability of this robotic EDM technology, this study focuses on a novel methodology to develop and validate a digital twin (DT) of the physical robotic EDM system. The digital twin functions as a virtual experimental environment for tool motion, effectively addressing the challenges posed by collisions and kinematic singularities inherent in the physical system, yet with proven 20-micron EDM gap accuracy. Furthermore, it facilitates a CNC-like, user-friendly offline programming framework for robotic EDM cutting path generation. Full article

The increasing need for materials that are both lightweight and strong in the aerospace and automotive sectors has driven the extensive use of composite sandwich structures. This study examines the impact response of honeycomb sandwich composites fabricated using the vacuum-assisted resin transfer molding (VARTM) technique. Two configurations were analyzed, namely carbon–honeycomb–carbon (CHC) and carbon–Kevlar–honeycomb–Kevlar–carbon (CKHKC), to assess the effect of Kevlar reinforcement on impact resistance. Charpy impact testing was conducted to evaluate energy absorption, revealing that CKHKC composites exhibited significantly superior impact resistance compared to CHC composites. The CKHKC composite achieved an average impact strength of 70.501 KJ/m², which is approximately 73.8% higher than the 40.570 KJ/m² recorded for CHC. This improvement is attributed to Kevlar’s superior toughness and energy dissipation capabilities. A comparative assessment of impact energy absorption further highlights the advantages of hybrid Kevlar–carbon fiber composites, making them highly suitable for applications requiring enhanced impact performance. These findings provide valuable insights into the design and optimization of high-performance honeycomb sandwich structures for impact-critical environments. Full article

This review presents a comprehensive synthesis of recent advances in the tensile performance of adhesively bonded joints, focusing on applied aspects and modeling developments rather than providing a full theoretical analysis. Although many studies have addressed individual joint types or modeling techniques, an integrated review that compares joint configurations, modeling strategies, and performance optimization methods under tensile loading remains lacking. This work addresses that gap by examining the mechanical behavior of key joint types, namely, single-lap, single-strap, and double-strap joints, and highlighting their differences in stress distribution, failure mechanisms, and structural efficiency. Modeling and simulation approaches, including cohesive zone modeling, extended finite element methods, and virtual crack closure techniques, are assessed for their predictive accuracy and applicability to various joint geometries. This review also covers material and geometric enhancements, such as adherend tapering, fillets, notching, bi-adhesives, functionally graded bondlines, and nano-enhanced adhesives. These strategies are evaluated in terms of their ability to reduce stress concentrations and improve damage tolerance. Failure modes, adhesive and adherend defects, and delamination risks are also discussed. Finally, comparative insights into different joint configurations illustrate how geometry and adhesive selection influence strength, energy absorption, and weight efficiency. This review provides design-oriented guidance for optimizing bonded joints in aerospace, automotive, and structural engineering applications. Full article

To address the challenges of manual inspection dependency, low efficiency, and high costs in evaluating the surface grinding quality of composite materials, this study investigated machine vision-based surface recognition algorithms. We proposed a multi-scale texture fusion analysis algorithm that innovatively integrated luminance analysis with multi-scale texture features through decision-level fusion. Specifically, a modified Rayleigh parameter was developed during luminance analysis to rapidly pre-segment unpolished areas by characterizing surface reflection properties. Furthermore, we enhanced the traditional Otsu algorithm by incorporating global grayscale mean (μ) and standard deviation (σ), overcoming its inherent limitations of exclusive reliance on grayscale histograms and lack of multimodal feature integration. This optimization enables simultaneous detection of specular reflection defects and texture uniformity variations. To improve detection window adaptability across heterogeneous surface regions, we designed a multi-scale texture analysis framework operating at multiple resolutions. Through decision-level fusion of luminance analysis and multi-scale texture evaluation, the proposed algorithm achieved 96% recognition accuracy with >95% reliability, demonstrating robust performance for automated surface grinding quality assessment of composite materials. Full article

Novel high-entropy perovskite oxide powders were synthesized using a sol-gel process. The B-site contained five cations: chromium, cobalt, iron, manganese, and nickel. The B-site cations were present on an equiatomic basis. The A-site cation was lanthanum, with calcium doping. The amount of A-site doping varied from 0 to 30 at%, yielding a composition of La_1−xCa_x(Co_0.2Cr_0.2Fe_0.2Mn_0.2Ni_0.2)O_3−δ. The resulting perovskite powders were pressurelessly sintered in air at 1400 °C for 2 h. Sintered densities were measured, and the grain structure was imaged via scanning electron microscopy to investigate the effect of doping. Samples were cut and polished, and their resistance was measured at varying temperatures in air to obtain the electrical conductivity and the mechanism that governs it. Plots of electrical conductivity as a function of composition and temperature indicate that the increased configurational entropy of the perovskite materials has a demonstrable effect. Full article

Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design. Full article