Alloys, Volume 5, Issue 2 (June 2026) – 5 articles

Commercial ductile steel sheet is widely used in engineering applications because of its favorable combination of strength, ductility, machinability, and mechanical versatility. However, its structural integrity is frequently compromised by geometric discontinuities, specifically notches. These stress raisers negatively affect fracture toughness and fatigue strength. The V-notch is particularly prevalent and critical in structural failure analysis. This study presents an experimental, numerical, and machine-learning investigation of the tensile properties of ductile steel sheet, focusing on various V-notch geometries. A systematic experimental study of 36 distinct specimens isolated the effects of geometric variations. Tensile tests were conducted in accordance with ASTM E8 standards to ensure reliability. Experimental results quantified tensile property degradation due to stress concentration, revealing severely compromised ductility in notched specimens. A validated numerical method subsequently captured necking and fracture behavior, reducing the need for expensive destructive experiments. Additionally, experimental data established a predictive framework for untested geometries. Random Forest, LightGBM, CatBoost, and XGBoost ensemble algorithms were employed for training and validation. Engineered features and hyperparameter tuning trained the models to effectively capture the nonlinear stress–strain region. CatBoost and LightGBM (R² = 0.9917 and 0.9902, respectively) provided the most stable predictive performance. This study establishes a validated baseline for V-notch behavior while demonstrating the effectiveness of data-driven models in minimizing extensive destructive testing. Full article

This study investigates the microstructural evolution of the CoCrFeNi system after incorporating Gallium (Ga) at varying concentrations (0, 15, and 20 at.%). The systems were synthesized by Vacuum Arc Melting (VAM) and characterized through X-ray Diffraction diffraction (XRD) and Scanning Electron Microscopy (SEM/EDS). Findings showed that the CoCrFeNi medium medium-entropy alloy stabilizes in a single-phase Face-Centered Cubic (FCC) structure. Upon the addition of 15 at.% Ga a dendritic morphology with a transition towards a duplex FCC + BCC microstructure was induced, a trend which was further solified in the equiatomic FeCoNiCrGa system. In this case the proportion of the Ga-rich BCC phase was increased from 18–22% to 31–34% for the Ga15 and Ga20 systems respectively. A combined approach of Electrochemical Frequency Modulation (EFM), Cyclic Potentiodynamic Polarization (CPP), and Electrochemical Impedance Spectroscopy (EIS) was selected for studying the electrochemical corrosion behavior of the produced systems. EFM results indicated a progressive deterioration of corrosion resistance when increasing Ga concentration (Icorr: 4.142, 5.619 and 10.01 μA/cm², and Rp: 12,035, 10,736 and 7254 Ω for the Ga0, Ga15 and Ga20 alloys respectively). Surface inhomogeneity, rapid passivation, and diffusion-controlled processes caused deviations from the ideal causality factors’ values. CPP measurements revealed increasing corrosion current densities with Ga addition within the Tafel region (2.81 × 10⁻⁷, 3.72 × 10⁻⁷ and 5.11 × 10⁻⁷A/cm² for the Ga0, Ga15 and Ga20 alloys respectively). All alloys showed positive hysteresis loops and an absence of repassivation, indicating susceptibility to pitting corrosion. Nevertheless, detailed analysis of the forward polarization region highlighted a more complex aspect. Reverse polarization scans confirmed stable pit growth in all alloys, with the absence of a repassivation tendency. EIS tests, performed after the completion of CPP measurements, further clarified the corrosion mechanisms. Equivalent circuit modeling revealed that although Ga-containing alloys exhibited relatively improved film characteristics in the forward polarization stage, the charge transfer resistance (Rct) was highest for the CoCrFeNi alloy, followed by Ga15 and Ga20 (22,620, 11,380, 10,060 Ω respectively). The overall impedance ranking (Ga0 > Ga15 > Ga20, i.e., 27,139 > 20,279.5 > 16,341 ohms respectively) showed that, despite microstructural and entropic effects enhancing certain passivation aspects, the reduced Cr content highly impacted long-term corrosion resistance. This holistic electrochemical approach showcases the complex interactions between compositional alterations, phase structure, grain refinement, passive film chemistry, and diffusion trends in establishing the corrosion performance of Ga-modified CoCrFeNi HEAs. Full article

This study investigates the possibility of producing a complex W–Mo–Cr-containing alloy via metallothermic reduction of oxide concentrates in the presence of direct reduced iron (DRI) in an induction furnace under atmospheric conditions. A complex FeAlSiCa alloy was used as a reductant due to its high exothermicity and combined reducing potential. Thermodynamic analysis showed that the reduction of WO₃ and MoO₃ is more favorable compared to Cr₂O₃, which is reflected in the temperature profiles of the process. Experimental results confirmed that the addition of FeAlSiCa leads to intensive exothermic reactions and promotes melt formation. The estimated apparent recovery of W and Mo reached up to ~99%, while Cr estimated apparent recovery remained lower (up to ~70%) due to its higher thermodynamic stability and kinetic limitations. Microstructural analysis revealed a heterogeneous structure consisting of an Fe-based matrix and W–Mo-rich phases, including characteristic “fishbone” morphologies. An increase in reductant amount led to higher Si content in the alloy, indicating the need for composition optimization. The results demonstrate the feasibility of direct complex alloying as an alternative to conventional ferroalloy-based methods and highlight the potential for developing resource-efficient and low-carbon metallurgical technologies. Full article

This study presents a comprehensive bibliometric analysis of research on 2xxx series aluminum–copper (Al–Cu) alloys published between 2020 and 2025. A complete analysis of 4380 documents from 747 sources indexed in Scopus reveals sustained research growth, with publications rising from 603 in 2020 to 948 in 2025 at a compound annual growth rate of 9.5%. China dominates global output, contributing 35.7% of publications with Central South University as the leading institution (548 articles). However, China’s international collaboration rate (12.2%) remains notably lower than Western counterparts such as the United Kingdom (62.5%) and Canada (53.2%). Core journals including the Journal of Alloys and Compounds, Materials Science and Engineering: A, and Journal of Materials Research and Technology collectively account for 11.4% of total publications, conforming to Bradford’s Law concentration patterns. Keyword co-occurrence analysis revealed five distinct thematic clusters centered on microstructure–property relationships, friction stir welding and joining technologies, corrosion mechanisms, Al–Cu–Li aerospace alloys, and additive manufacturing. While life cycle modeling (K = 5993; tm = 2022.84) indicates the field is approaching maturity, by identifying emerging frontiers such as machine learning-assisted alloy design, sustainable processing routes, and multi-material joining for electric vehicles, this study offers researchers a quantitative roadmap of the Al–Cu alloy knowledge landscape and highlights strategic opportunities for future investigation. Full article

The development of high-performance copper alloys requires balancing mechanical strength and electrical conductivity, properties that are often inversely correlated due to competing strengthening mechanisms. This study presents a comparative machine learning analysis of Cu-Ni-Si and Cu low alloys using a curated dataset of 1690 entries derived from the Gorsse et al. database, comprising 1507 samples with hardness measurements and 1685 samples with electrical conductivity data. Three ensemble-based regression algorithms, Random Forest, XGBoost, and Gradient Boosting, were trained to predict Vickers hardness (HV) and electrical conductivity (%IACS) from an augmented feature set encompassing alloy composition, thermomechanical processing parameters, missingness indicators, and physics-informed descriptors (valence electron concentration, atomic size mismatch, electronegativity difference, and Ni:Si atomic ratio). XGBoost achieved optimal performance for hardness prediction (R² = 0.8554, RMSE = 29.90 HV), while Gradient Boosting performed best for electrical conductivity (R² = 0.8400, RMSE = 5.96%IACS). Averaged tree-based feature-importance analysis identified valence electron concentration as the most influential predictor for hardness (39.9%), followed by aging temperature (11.2%), while Cu content dominated conductivity prediction (37.7%), followed by aging time (8.9%). Complementary SHAP analysis confirmed these trends while revealing directional relationships and nonlinear feature interaction effects. Composition-grouped cross-validation by unique alloy formula (K = 10) yielded substantially lower performance, with grouped CV R² = 0.438 for hardness and 0.293 for conductivity, indicating that generalization to unseen alloy formulations remains limited. The models were further applied for practical tasks, including property prediction for new alloy compositions, processing parameter optimization via differential evolution with metallurgical constraints (achieving hardness up to 293.9 HV or conductivity up to 45.7%IACS for the same base composition, with prediction intervals reported), and inverse design to identify alloy formulations meeting specified target properties. This work demonstrates the potential of interpretable machine learning to support copper alloy development by enabling rapid computational screening of the compositional and processing parameter space, subject to the generalization limitations identified herein. Full article