Search Results (3,622)

The micromechanisms “slip transfer, slip irreversibility, microplasticity, and fatigue cracking” in titanium alloys are reviewed, with a special emphasis on near-α and α + β alloys. As the interplay between slip activity, microplasticity, and fatigue cracking governs both the microscale and macroscale mechanical response, we reveal how the slip irreversibility and localized dislocation activity at the grain boundaries (GBs) and α/β interfaces generate dislocation pile-ups and strain localization, subsequently driving fatigue crack initiation and propagation. The review highlights the favorable crack initiation along basal planes and the roles of α grain orientations, slip transfer barriers, and the β phase in governing fatigue cracking, while addressing unresolved questions about localized interactions and texture effects. It also explores the complex interactions that govern the effects of microstructures, textures, and defects on fatigue cracking. Ultimately, the review provides a unified framework for linking slip events to microplasticity and to fatigue failure, offering actionable insights for alloy design and fatigue prediction. Full article

This study investigates the synergistic effects of Cr–Zr and Mn–Zr additions on the microstructural evolution and mechanical properties of Al–6 wt.%Cu alloys. Alloys were designed with solute concentrations positioned below, near, and above their maximum solubility limits, and were evaluated under as-cast, T4, and T6 heat treatment conditions. Mechanical testing revealed distinct behavioral trends depending on the heat treatment: the T4 heat treatment condition generally exhibited superior hardness and yield strength, whereas the T6 heat treatment condition resulted in a slight reduction in hardness but facilitated a significant recovery in tensile strength and structural stability, particularly in alloys designed near the solubility limit. To elucidate the crystallographic origins of these mechanical variations, X-ray diffraction analysis was conducted to monitor changes in lattice parameters, dislocation density, and micro-strain. The results showed that T4 heat treatment induced lattice contraction and a decrease in dislocation density, suggesting that the high strength under T4 heat treatment conditions arises from lattice distortion caused by supersaturated solute atoms. Conversely, T6 aging led to lattice relaxation approaching that of pure aluminum, yet simultaneously triggered a re-accumulation of dislocation density and micro-strain due to the coherency strain fields surrounding precipitates, which effectively impede dislocation motion. Therefore, rather than proposing a single, definitive optimization condition, this study aims to secure foundational data regarding the correlation between these microstructural descriptors and mechanical behavior, providing a guideline for balancing the strengthening contributions in transition metal-modified Al–Cu alloys. Full article

Fe-Ni-based alloys have attracted attention due to their potential for applications such as transmission line de-icing, where the core requirements include a Curie temperature near the freezing point and sufficient saturation magnetization. Accordingly, this study designed an Fe-29Ni-2Cr-1.5Mn (at.%) alloy with a Curie temperature around the freezing point, aiming to investigate the correlation between microstructural evolution and magnetic properties after cold rolling and annealing. The alloy was cold-rolled by 65% and subsequently annealed at 873 K for 0 to 60 min. The study reveals systematic evolutions in the alloy’s microstructure and magnetic properties. During the initial annealing stage, recovery substructures predominantly formed within the deformed grains, accompanied by a reduction in dislocation density and lattice constant. In the later annealing stage, the recrystallized fraction increased, although complete recrystallization was not achieved. Texture analysis indicates that the intensity of the Cube texture strengthened from 0.48 to 1.13. Correspondingly, the saturation magnetization and Curie temperature increased by approximately 9.76% and 10.25%, respectively, in the early annealing period, and then stabilized thereafter. The early-stage improvement in properties is likely related to stress relief and lattice distortion relaxation during the recovery stage. The calculated magnetocrystalline anisotropy constant of this alloy at 273 K is K₁ = 126 ± 18 J/m³, indicating that the <100> direction is its easy magnetization axis. This study provides insights into optimizing the magnetic properties of this alloy through controlled annealing. Full article

When fabricating high-entropy alloy particle-reinforced metal matrix composites via friction stir processing, the relatively low heat input led to insufficient interfacial diffusion between the particles and matrix, thereby compromising the composite properties. To address this issue, this study introduced an electroless copper plating step followed by heat treatment to produce Cu-coated HEA particles with an interfacial diffusion layer. These modified particles were then incorporated into a copper matrix via friction stir processing to form composites with an intentionally designed interfacial diffusion layer. The results indicate that the diffusion layer structure contributed to excellent interfacial bonding. The resulting composite exhibited a simultaneous enhancement in both strength and ductility. The tensile strength and elongation reached 372.5 MPa and 34.2%, respectively, representing increases of 20.4% and 54% compared to pure copper. The wear rate of the composite reduced by 33.7% relative to pure copper. Quantitative analysis indicated that the contribution of fine-grain strengthening, Orowan strengthening, dislocation strengthening, and load transfer strengthening to the overall strength was 41.2 MPa, 0.3 MPa, 12.7 MPa, and 15.7 MPa, respectively. Full article

Direct ethanol fuel cells (DEFCs) have gained considerable attention as promising power sources for sustainable energy conversion due to their high energy density, low toxicity, and renewable ethanol feedstock. However, the sluggish ethanol oxidation reaction (EOR) kinetics and the formation of strongly adsorbed intermediates (e.g., CO*, CH_x*) severely hinder catalytic efficiency and durability. Rhodium (Rh)-based catalysts stand out for their balanced intermediate adsorption, efficient C–C bond cleavage, and superior CO tolerance arising from their unique electronic structure. This review summarizes recent advances in Rh-based EOR catalysts, including monometallic Rh nanostructures, Rh-based alloys, and Rh–support composites. The effects of morphology, alloying, and metal–support interactions on activity, selectivity, and stability are discussed in detail. Strategies for structural and electronic regulation—such as nanoscale design, alloying modulation and interfacial engineering—are highlighted to enhance catalytic performance. Finally, current challenges and future directions are outlined, emphasizing the need for Rh-based catalysts with high activity, selectivity and stability, integrating in situ characterization with theoretical modeling. This work provides insights into the structure–activity relationships of Rh-based catalysts and guidance for designing efficient and durable anode catalysts for practical DEFC applications. Full article

The strategic balance between strength and electrical conductivity in Al-Mg-Si alloys is a critical challenge that must be overcome to enable their widespread adoption as viable alternatives to copper conductors in power transmission systems. To address this, the present study comprehensively investigates model alloys with Mg/Si ratios ranging from 1.0 to 2.0. A multi-faceted experimental approach was employed, combining tailored thermo-mechanical treatments (solution treatment, cold drawing, and isothermal annealing) with comprehensive microstructural characterization techniques, including electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM). The results elucidate a fundamental competitive mechanism governing property optimization: excess Mg atoms concurrently contribute to solid-solution strengthening via the formation of Cottrell atmospheres around dislocations, while simultaneously enhancing electron scattering, which is detrimental to conductivity. A critical synergy was identified at the Mg/Si ratio of 1.75, which promotes the dense precipitation of fine β″ phase while facilitating extensive recovery of high dislocation density. Furthermore, EBSD analysis confirmed the development of a microstructure comprising 74.1% high-angle grain boundaries alongside a low dislocation density (KAM ≤ 2°). This specific microstructural configuration effectively minimizes electron scattering while providing moderate grain boundary strengthening, thereby synergistically achieving an optimal balance between strength and electrical conductivity. Consequently, this work elucidates the key quantitative relationships and competitive mechanisms among composition (Mg/Si ratio), processing parameters, microstructure evolution, and final properties within the studied Al-xMg-0.5Si alloy system. These findings establish a clear design guideline and provide a fundamental understanding for developing high-performance aluminum-based conductor alloys with tailored Mg/Si ratios. Full article

Accurate prediction of ultimate tensile strength (UTS) is central to the design and optimization of heat-treated steels but is traditionally achieved through costly and iterative experimental trials. This study presents a transparent, physics-aware machine learning (ML) framework for predicting UTS using an open-access steel database. A curated dataset of 1255 steel samples was constructed by combining 18 chemical composition variables with 7 processing descriptors extracted from free-text heat-treatment records and filtering them using physically justified consistency criteria. To avoid information leakage arising from repeated measurements, model development and evaluation were conducted under a group-aware validation framework based on thermomechanical states. A Random Forest (RF) regression model achieved robust, conservative test-set performance (R² ≈ 0.90, MAE ≈ 40 MPa), with unbiased residuals and realistic generalization across diverse composition–processing conditions. Performance robustness was further examined using repeated group-aware resampling and strength-stratified error analysis, highlighting increased uncertainty in sparsely populated high-strength regimes. Model interpretability was assessed using SHAP-based feature importance and partial dependence analysis, revealing that UTS is primarily governed by the overall alloying level, carbon content, and processing parameters controlling transformation kinetics, particularly bar diameter and tempering temperature. The results demonstrate that reliable predictions and physically meaningful insights can be obtained from publicly available data using a conservative, reproducible machine-learning workflow. Full article

Machine learning has been widely applied to phase prediction and property evaluation in multi-principal element alloys. In this work, a data-driven machine learning framework is proposed to predict the ultimate tensile strength (UTS) and total elongation (TE) of Fe-Co-Cr-Ni-Mn-Al-Ti multi-principal element alloys (MPEAs), offering a cost-effective route for the design of new MPEAs. A dataset was compiled through an extensive literature survey, and six different machine learning models were benchmarked, from which XGBoost was ultimately selected as the optimal model. The feature set was constructed on the basis of theoretical considerations and experimental data reported in the literature, and SHAP analysis was employed to further elucidate the relative importance of individual features. By imposing constraints on the screened features, two alloys predicted to exhibit superior performance under different heat-treatment conditions were identified and fabricated for experimental validation. The experimental results confirmed the reliability of the model in predicting fracture strength, and the errors observed in ductility prediction were critically examined and discussed. Moreover, the strengthening mechanisms of the designed MPEAs were further explored in terms of microstructural characteristics and lattice distortion effects. The alloy design methodology developed in this study not only provides a theoretical basis for exploring unexplored compositional spaces and processing conditions in multi-principal element alloys, but also offers an effective tool for developing novel alloys that simultaneously achieve high strength and good ductility. Full article

The study is dedicated to the accuracy of engineering analyses of virtual prototypes. In particular, it aims to quantify the importance of material models and data consistent with physical tests. The focus is set on the stress–strain material characteristic that is the basis for correct simulation results, and the deviations of its parameters—elasticity module and yield stress—that are assessed. This is performed in three main steps: laboratory measurement of the material properties of a sample material (aluminum alloy), followed by an engineering analysis of a component produced from the same material, using the determined mechanical characteristics. The third step involves physical tests used to validate the virtual prototyping results by comparing them with the physical test results. The material properties used in the virtual prototype are subjected to a sensitivity analysis to determine their influence on the design’s elastic and plastic behavior. The main conclusions of the study are the importance of these material characteristics for achieving an adequate result. A general recommendation is formed that shows the importance of laboratory testing of material properties before virtual prototyping to avoid any influence of factors as production technology or geometry (specimen thickness). Full article

Manganese is a critical raw material and there is currently a great interest in decarbonization in the metallurgical sector for its production. Hydrogen use in manganese and its alloys’ production is in principle possible for sustainable production; however, this requires a technological shift from traditional carbothermic processes to completely new processes; like the HAlMan process. To design a process, it is crucially important to optimize the process conditions (such as temperature) and minimize the quantity of hydrogen gas and the related energy consumptions. In the present work, energy and mass balances for a hydrogen-based reduction reactor were carried out employing thermodynamics software and analytical approaches from room temperatures to 900 °C. It was found that the quantity of hydrogen gas required for the pre-reduction of manganese ore can be significantly reduced via coupling the reduction reactor with a calciner and the hot charge of the calcined ore into the reduction reactor. Moreover, hot H₂-H₂O gas mixture from the reduction reactor outlet can be used for preheating the hydrogen feed of the reactor, and the calcination of the ore, while a portion or all its hydrogen can be recovered and looped. The integrated coupled calcination-reduction process was found to be operated with no external energy supply, or insignificant fuel use. Full article

Measuring the fatigue crack growth rate via the crack growth experiment (a-N curve) is labor-intensive and time-consuming. A machine learning interpolation–extrapolation strategy (MLIES) aimed at enhancing the prediction accuracy of small-data models has been proposed to accelerate fatigue testing. Two specific approaches are designed by transforming a-N curve data from N to ΔN and from a to Δa (S1)/Δa/ΔN (S2) to enrich the data volume and leverage the incremental information. Thus, a simple and fast-responding single-layer neural network model can be trained based on the early-stage data points from fatigue testing and accurately predict the remaining part of an a-N curve, thereby enhancing the experimental efficiency. Through exponential data expansion and data augmentation, the trained neural network model is able to learn the underlying rules governing crack growth directly from the experimental data, requiring no explicit analytical crack growth laws. The proposed MLIES was validated on fatigue tests for aluminum alloy and titanium alloy samples under different experimental parameters. Results demonstrate its effectiveness in reducing testing time/cost by 15–32% while achieving over 30% higher prediction accuracy for the a-N curve compared to a traditional machine learning modeling approach. Our research offers a data-driven recipe for accurate crack growth prediction and accelerated fatigue testing. Full article

The design of ladle furnace (LF) refining pathways for weathering steels requires precise control of multi-component steel/slag reactions governed simultaneously by thermodynamics and interfacial mass transfer kinetics. An EERZ-based kinetic modeling strategy was employed using the Thermo-Calc^® (version 2022a) Process Metallurgy Module and the CALPHAD TCOX11 database to develop LF refining schedules capable of upgrading conventional S355J2R steel to weathering steel grades: S355J2W and S355J2WP. First, the sensitivity of predicted compositions to key kinetic inputs was quantified. The validated model was then used to simulate deoxidation and desulfurization sequences, predicting the evolution of liquid–steel and slag compositions, slag basicity, and FeO activity throughout the LF cycle. Subsequently, Cr- and P-ferroalloys were introduced to design tap-to-tap schedules that meet the EN 10025-5 chemical specifications for S355J2W and S355J2WP. To correlate simulation outcomes with material performance, plates produced following the modeled schedules were evaluated through a 1000 h accelerated salt spray test. Steel density and steel phase mass transfer coefficients were found to produce the highest prediction sensitivity (up to 7.5 wt.% variation in C and S), whereas slag phase parameters exhibited a lower impact. The predicted steel compositions showed strong agreement with industrial values obtained during plant trials. SEM-EDS analyses confirmed the development of a Cr-enriched protective patina and validated model-based alloying strategies. Full article

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper presents a novel, bioinspired, and integrated prosthetic system as an advancement in bionic technology. The design incorporates a shoulder joint based on an asymmetric 3-RRR spherical parallel mechanism (SPM) with actuators embedded within the moving platform, and an elbow joint actuated by low-voltage Shape Memory Alloy (SMA) springs. The inverse kinematics of the shoulder mechanism was established, revealing the existence of up to eight configurations. We employed Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously maximize workspace coverage, enhance dexterity, and minimize joint torque. The optimized design achieves remarkable performance: (1) 85% coverage of the natural shoulder’s workspace; (2) a maximum von Mises stress of merely 3.4 MPa under a 40 N load, ensuring structural integrity; and (3) a sub-0.2 s response time for the SMA-driven elbow under low-voltage conditions (6 V) at a motion velocity of 6°/s. Both motion simulation and prototype testing validated smooth and anthropomorphic motion trajectories. This work provides a comprehensive framework for developing lightweight, high-performance prosthetic limbs, establishing a solid foundation for next-generation wearable robotics and bionic devices. Future research will focus on the integration of neural interfaces for intuitive control. Full article

Unmanned Aerial Vehicle (UAV)-based inspection is crucial for the maintenance and monitoring of high-voltage transmission lines, but detecting small objects in inspection images presents significant challenges, especially under complex backgrounds and varying lighting. These challenges are particularly evident when detecting the wire features of optical fiber composite overhead ground wire and conventional ground wires. Optical fiber composite overhead ground wire (OPGW) is a specialized cable designed to replace conventional shield wires on power utility towers. It contains one or more optical fibers housed in a protective tube, surrounded by layers of aluminum-clad steel and/or aluminum alloy wires, ensuring robust mechanical strength for grounding and high-bandwidth capabilities for remote sensing and control. Existing detection methods often struggle with low accuracy, insufficient performance, and high computational demands when dealing with small objects. To address these issues, this paper proposes an energy-efficient OPGW feature detection model for UAV-based inspection. The model incorporates a Feature Enhancement Module (FEM) to replace the C3K2 module in the sixth layer of the YOLO11 backbone, improving multi-scale feature extraction. A P2 shallow detection head is added to enhance the perception of small and edge features. Additionally, the traditional Intersection over Union (IoU) loss is replaced with Normalized Wasserstein Distance (NWD) loss function, which improves boundary regression accuracy for small objects. Experimental results show that the proposed method achieves a ${\mathrm{mAP}}_{50}$ of 78.3% and ${\mathrm{mAP}}_{50–95}$ of 52.0%, surpassing the baseline by 2.3% and 1.1%, respectively. The proposed model offers the advantages of high detection accuracy and low computational resource requirements, providing a practical solution for sustainable UAV-based inspections. Full article

Medium-entropy alloys (MEAs) represent a promising class of materials for solid-state hydrogen storage due to their high hydrogen affinity, structural stability, and tunable properties. In this work, a compositional series of (TiZrNbTa)_{100−x}Al_x (x = 0–10 at. %) MEAs were prepared and systematically investigated to clarify the influence of aluminum addition on microstructure, mechanical response, and hydrogen sorption behavior. The alloys were synthesized by arc melting, homogenized by annealing, and characterized using microscopy, X-ray diffraction, density measurements, microhardness testing, nanoindentation, and hydrogen absorption/desorption experiments. Hydrogen sorption was evaluated by isobaric absorption measurements at 2 MPa H₂ over two consecutive cycles, complemented by thermogravimetric desorption analysis of hydrogenated samples. The results show that aluminum addition significantly affects activation behavior, hydrogen uptake, and residual hydrogen retention, while simultaneously increasing hardness and elastic modulus in a non-linear manner. The alloy containing 5 at. % Al exhibits the most balanced performance, combining reduced activation temperature in the second absorption cycle, relatively high hydrogen capacity, and moderate mechanical stiffness. These findings demonstrate that controlled aluminum alloying is an effective strategy for tailoring hydrogen–metal interactions and optimizing the performance of TiZrNbTa-based MEAs for solid-state hydrogen storage applications. Full article