Research Digest: Advances in Alloys and Metallurgical Systems

Machine Learning-Driven Comparative Analysis and Optimization of Cu-Ni-Si and Cu Low Alloys: From Data-Driven Interpretation to Inverse Design

Mihail Kolev — 2026-05-12

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.

Production and Characterization of Al Alloys Obtained Through Molten Metal Deposition

Cinzia Menapace — 2026-02-27

Two aluminum alloys (4043 and 6061) were fabricated using the innovative Molten Metal Deposition (MMD) technique. Three types of samples were produced by varying selected deposition parameters. The quality of the resulting components was assessed in terms of defects, density, and microstructure. In the 4043 alloy, the microstructure consists of α-Al dendrites surrounded by an Al–Si eutectic phase. All 4043 samples exhibited this microstructure, regardless of the deposition parameters. The mechanical response was preliminarily evaluated through HV0.5 microhardness measurements. The indentations produced under a 500 g load enabled the assessment of the contribution of both the α-Al matrix and the surrounding Al–Si eutectic. As for the 6061 alloy, its microstructure is composed of an α-Al matrix containing dispersed Al–Si–Fe intermetallics. Some oxide particles were observed at the grain boundaries, indicating the need for processing under a controlled atmosphere. In this study, no inert shielding atmosphere was used for the fabrication of the samples. Thanks to its high processing speed, sustainability, and ease of deployment, MMD can be regarded as a viable alternative to more conventional additive manufacturing technologies.

Compositional Design of High-Entropy Alloys: Advances in Structural and Hydrogen Storage Materials

Shaopeng Wu — 2026-02-04

High-entropy alloys (HEAs) present a vast compositional design space, characterized by four core effects—high configurational entropy, sluggish diffusion, severe lattice distortion, and the cocktail effect—which collectively underpin their exceptional potential for both structural and hydrogen storage applications. This mini-review synthesizes recent advances in the compositional design of HEAs with emphasis on structural materials and hydrogen storage. Firstly, it provides an overview of the definition of HEAs and the roles of principal alloying elements, then synthesizes solid solution formation rules based on representative descriptors—atomic size mismatch, electronegativity difference, valence electron concentration, mixing enthalpy, and mixing entropy—together with their applicability limits and common failure scenarios. A brief introduction is provided to the preparation methods of arc melting and powder metallurgy, which have a strong interaction with the composition. The design–structure–property links are then consolidated for structural materials (mechanical properties) and for hydrogen storage materials (hydrogen storage performance). Furthermore, the rules for the combined design of control systems for HEAs and the associated challenges were further discussed, and the future development prospects of HEAs in structural materials and hydrogen storage were also envisioned.

The Experimental Determination of Parameters for the Modeling of the Stamping Process of AA6005C Aluminum Alloy

Luiza Emília Vila Nova Mazzoni — 2026-05-01

This study provides the first complete and experimentally validated Yoshida–Uemori (Y–U) parameter set for AA6005C aluminum alloy, enabling accurate constitutive modeling for stamping simulations. A comprehensive set of mechanical tests was conducted, comprising uniaxial tensile tests along 0^◦, 45^◦, and 90^◦ to the rolling direction, hydraulic bulge tests, Nakajima tests for the forming limit curve (FLC), and cyclic tension-compression experiments. Results showed moderate planar anisotropy with R-values of 0.49–0.90, equi-biaxial yield stress around 105 MPa, and plane-strain FLC₀ ≈ 0.25, typical for 6xxx-series alloys. The cyclic tests highlighted a strong Bauschinger effect and transient softening, which allowed precise calibration of the Yoshida-Uemori (Y-U) model. The resulting material parameters were validated using a U-bending case study, in which the predicted springback angle differed by only 2^◦, confirming the transferability of the calibrated model to forming conditions not used during parameter identification. The dataset generated in this work provides a robust foundation for finite element simulations of the AA6005C stamping processes and constitutes a practical reference for industrial implementation.

Tribological Performance of Micro and Nano-Titanium Carbide-Reinforced Copper Composites Manufactured by Powder Metallurgy: Experimental Studies and Modelling

Anwar Ulla Khan — 2026-02-06

This study reports the fabrication of copper-based metal matrix composites reinforced with a combination of micro- and nano-sized titanium carbide (TiC) particles using the powder metallurgy route. The micro-TiC content was maintained at 5 wt.%, while the nano-TiC addition was systematically varied between 1 and 3 wt.% in increments of 1 wt.%. The consolidation of the blends was achieved by uniaxial compaction at 500 MPa, followed by sintering in a nitrogen atmosphere at 750–900 ◦C for 2 h. Tribological assessment under dry sliding conditions was performed using a pin-on-disk apparatus. Structural and microstructural examinations using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS) confirmed a uniform incorporation of the reinforcements within the Cu matrix. The incorporation of nano-TiC up to 2 wt.% significantly enhanced density, hardness, and wear resistance, after which a marginal decline was observed. SEM analysis of worn surfaces revealed that adhesive wear, abrasion, and delamination were the primary wear mechanisms. To better understand the relationship between processing conditions and material responses, response surface methodology (RSM) was employed. The developed models for density, hardness, and wear loss showed good agreement with the experimental results, with confirmatory tests yielding errors of 1.59%, 2.06%, and 2%, respectively, thereby validating the approach’s reliability.