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.

Multiple advantages, such as good formability, high specific strength, excellent thermal conductivity, and high corrosion resistance, enable aluminum alloy wide application in various fields; however, low surface hardness and poor wear resistance limit its further development. In this study, three surface modification coatings were successfully prepared on the surface of 7A04 aluminum alloy by microarc oxidation (MAO) and a combination of hard anodizing treatment (HA) and physical vapor deposition (PVD), named MAO, HA+W+DLC, and HA+Ti+ta-C, respectively. The microstructure, hardness, and tribological properties of the three coatings and the 7A04 aluminum alloy substrate were studied. The results show that the surface quality and hardness of the coated samples were higher than those of the 7A04 aluminum alloy and that the HA+Ti+ta-C coating possessed the highest hardness of 34.23 GPa. Moreover, the wear resistance of the two multilayer coatings was significantly improved during the ring-block wear tests under oil lubrication, exhibiting a wear rate of 1.51 × 10⁻⁷ mm³/N·m for HA+W+DLC and 1.36 × 10⁻⁷ mm³/N·m for HA+Ti+ta-C.

The 7A04 Al alloy is a commonly used lightweight metal material; however, its low wear resistance limits its application. In this study, the wear resistance of this alloy was improved by preparing micro-arc oxidation (MAO) coatings, MAO/MoS₂ composite coatings, and hard-anodized (HA) coatings on its surface. The friction and wear behaviors of these three coatings with diamond-like coated (DLC) rings under oil lubrication conditions were investigated using a ring–block friction tester. The wear rates of the coatings on the block surfaces were determined using laser confocal microscopy, and the wear trajectories of the coatings were examined using scanning electron microscopy. The results indicated that, among the three coatings, the MAO/MoS₂ coating had the lowest coefficient of friction of 0.059, whereas the HA coating had the lowest wear rate of 1.47 × 10⁻⁶ mm/Nm. The MAO/MoS₂ coatings exhibited excellent antifriction properties compared to the other coatings, whereas the HA coatings exhibited excellent anti-wear properties. The porous structure of the MAO coatings stored lubricant and replenished the lubrication film under oil lubrication. Meanwhile, the introduced MoS₂ enhanced the densification of the coating and functioned as a solid lubricant. The HA coating exhibited good wear resistance owing to the dense structure of the amorphous-phase aluminum oxide. The mechanisms of abrasive and adhesive wear of the coatings under oil lubrication conditions and the optimization of the tribological properties by the solid–liquid synergistic lubrication effect were investigated. This study provides an effective method for the surface modification of Al alloys with potential applications in the aerospace and automotive industries.