Aluminum alloys are widely used due to their exceptional properties, but the systematic relationship between their grain size and their tensile strength has not been thoroughly explored in the literature. This study aims to fill this gap by compiling a comprehensive dataset and utilizing machine learning models that consider both the alloy composition and the grain size. A pivotal enhancement to this study was the integration of hardness as a feature variable, providing a more robust predictor of the tensile strength. The refined models demonstrated a marked improvement in predictive performance, with XGBoost exhibiting an R² value of 0.914. Polynomial regression was also applied to derive a mathematical relationship between the tensile strength, alloy composition, and grain size, contributing to a more profound comprehension of these interdependencies. The improved methodology and analytical techniques, validated by the models’ enhanced accuracy, are not only relevant to aluminum alloys, but also hold promise for application to other material systems, potentially revolutionizing the prediction of material properties.

The oxidation of 3Cr3Mo2NiW and 3CrNi3Mo steels was studied at 600 °C in air, and the test results suggest that the parabolic rate law fitted the oxidation kinetics of both steels. The microstructure, morphology, structure, and phase composition of the oxide film cross-sectional layers of the two Cr-Ni-Mo hot-work die steels were analyzed using scanning electron microscopy (SEM), energy-dispersive spectrometry (EDS), and X-ray diffraction (XRD). The influences of Cr, Ni, and Mo on the high-temperature oxidation resistance of the two Cr-Ni-Mo hot-work die steels are discussed, and the oxidation mechanism is summarized. Heat-treated samples were analyzed using electron backscattered diffraction (EBSD) to obtain inverse pole figures (IPFs) and average sample grain sizes, and the percentages of twin grain boundaries (TGBs) (θ = 60°) were also measured. After heat treatment, recrystallization was observed in both steels with a large portion of twin grain boundaries. After 10 h of oxidation, the dense chromium-rich oxide layer that formed in the inner oxide layer of 3Cr3Mo2NiW steel effectively prevented the continuation of oxidation. The inner oxide layer in 3CrNi3Mo steel formed an adhesion layer with a network structure composed mainly of Ni- and Cr-rich spinel oxide, without forming a barrier to prevent oxidation.

This study investigates the combustion characteristics and critical thermodynamic conditions for the ignition of TC4 and TC17 alloys under high-speed friction conditions. The results indicate that, under identical rubbing conditions, both the critical pressure and the ignition temperature of the TC17 alloy are higher than those of the TC4 alloy. The critical ignition conditions for both alloys increase with thickness, while they decrease with increasing rotational speed, oxygen concentration, and oxygen pressure. The primary characteristics in the initial stage of friction ignition are abrasive and adhesive wear. As the duration of friction increases, material from the friction surface begins to peel away, creating favorable conditions for ignition. At the moment of ignition, significant peeling occurs, along with visible cracks and molten structures, resulting in the production of a substantial amount of titanium oxide on the friction surface. Based on the ignition theory proposed by Frank-Kamenetskii, the reaction order, adsorption coefficient, pre-exponential factor, and activation energy of the ignition criterion under high-speed friction conditions were determined by fitting and analyzing the experimental results. The ignition temperatures of the TC4 and TC17 alloys at different speeds were predicted with a relative error of less than 2.06%. This demonstrates that the Frank-Kamenetskii model can be utilized to explain the critical ignition conditions of titanium alloys under high-speed rubbing conditions.