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.

Metallic materials are always suffered from the risk of combustion when serviced under some extreme conditions such as high temperature, oxygen-enriched enrichment, and high-speed friction. Although different mathematic models have been proposed but it is still a challenge for accurately describing the ignition conditions of metals under the extreme conditions, which is of great significance for the safety-use of materials. In this paper, the mathematic models based on Semenov and Frank-Kamenetskii theory were introduced into describing the effects of size, oxygen concentration, and oxygen pressure on the ignition temperature and critical oxygen pressure of TC17 alloy. The results showed that the critical oxygen pressure of TC17 alloy increased with the increase of size, which was fitted well with the Frank-Kamenetskii model. As a comparison, the critical oxygen pressure was size independent in the Semenov model, which was inconsistent with the experimental data. For the Frank-Kamenetskii model, the fitting results showed that the activation energy, reaction order, as well as the adsorption coefficient of TC17 alloy were determined to be 99.23 kJ/mol, 1.69, and 4.01 MPa^−1.69 respectively. Based on above, the ignition temperature of TC17 samples with different sizes were predicted well by the Frank-Kamenetskii model with the relative error within 3.58%, which could be suitable for describing the critical ignition conditions of bulk metallic materials under complex environment.