Design and Development of Stable Nanocrystalline High-Entropy Alloy: Coupling Self-Stabilization and Solute Grain Boundary Segregation Effects.

Grain growth is prevalent in nanocrystalline (NC) materials at low homologous temperatures. Solute element addition is used to offset excess energy that drives coarsening at grain boundaries (GBs), albeit mostly for simple binary alloys. This thermodynamic approach is considered complicated in multi-component alloy systems due to complex pairwise interactions among alloying elements. Guided by empirical and GB-segregation enthalpy considerations for binary-alloy systems, a novel alloy design strategy, the "pseudo-binary thermodynamic" approach, for stabilizing NC-high entropy alloys (HEAs) and other multi-component-alloy variants is proposed. Using Al25Co25Cr25Fe25 as a model-HEA to validate this approach, Zr, Sc, and Hf, are identified as the preferred solutes that would segregate to HEA-GBs to stabilize it against growth. Using Zr, NC-Al25Co25Cr25Fe25 HEAs with minor additions of Zr are synthesized, followed by annealing up to 1123 K. Using advanced characterization techniques- in situ X-ray diffraction (XRD), scanning/transmission electron microscopy (S/TEM), and atom probe tomography, nanograin stability due to coupling self-stabilization and solute-GB segregation effects is reported in HEAs up to substantially high temperatures. The self-stabilization effect originates from the preferential GB-segregation of constituent HEA-elements that stabilizes NC-Al25Co25Cr25Fe25 up to 0.5Tm (Tm-melting temperature). Meanwhile, solute-GB segregation originates from Zr segregation to NC-Al25Co25Cr25Fe25 GBs; this results in further stabilization of the phase and grain-size (≈14 nm) up to ≈0.58 and ≈0.64Tm, respectively.

Nanotwinning-assisted dynamic recrystallization at high strains and strain rates.

Grain refinement is a widely sought-after feature of many metal production processes and frequently involves a process of recrystallization. Some processing methods use very high strain rates and high strains to refine the grain structure into the nanocrystalline regime. However, grain refinement processes are not clear in these extreme conditions, which are hard to study systematically. Here, we access those extreme conditions of strain and strain rate using single copper microparticle impact events with a laser-induced particle impact tester. Using a combined dictionary-indexing electron backscatter diffraction and scanning transmission electron microscopy approach for postmortem characterization of impact sites, we systematically explore increasing strain levels and observe a recrystallization process that is facilitated by nanotwinning, which we term nanotwinning-assisted dynamic recrystallization. It achieves much finer grain sizes than established modes of recrystallization and therefore provides a pathway to the finest nanocrystalline grain sizes through extreme straining processes.

Recent advances on bonding mechanism in cold spray process: A review of single-particle impact methods.

Cold spray (CS) processing is a layer-by-layer solid-state deposition process in which particles at a temperature below their melting point are launched to sufficiently high velocities to adhere to a substrate (and previously deposited particles), forming coatings/parts. Despite being in existence for over four decades, particle bonding mechanisms in the CS process are unclear due to the complex particle-particle/carrier gas interactions that obscure assessment. This review evaluates recent findings from single-particle impact approaches that circumvent these complexities and further provide new insights on bonding mechanisms. Theories on the evolution of oxide layer breakup and delamination, adiabatic shear instability, jetting, melting, and interface solid-state amorphization that contributes to bonding are assessed and carefully reviewed. Although there is a unified condition in which bonding sets on, this study shows that no singular theory explains bonding mechanism. Rather, dominant mechanism is a function of the prevailing barriers unique to each impact scenario.

Data-driven analysis and prediction of stable phases for high-entropy alloy design.

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.