Fu Keya, Zhu Dexin, Zhang Yuqi, Zhang Cheng, Wang Xiaodong, Wang Changji, Jiang Tao, Mao Feng, Zhang Cheng, Meng Xiaobo, Yu Hua
School of Electrical & Information Engineering, Beihang University, No. 37, Xueyuan Road, Beijing 100191, China.
Beijing Advanced Innovation Center for Materials Genome Engineering, Innovation Research Institute for Carbon Neutrality, University of Science and Technology Beijing, No. 30, Xueyuan Road, Beijing 100083, China.
Materials (Basel). 2023 Nov 20;16(22):7236. doi: 10.3390/ma16227236.
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 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.
铝合金因其优异的性能而被广泛应用，但它们的晶粒尺寸与抗拉强度之间的系统关系在文献中尚未得到充分探讨。本研究旨在通过汇编一个全面的数据集并利用考虑合金成分和晶粒尺寸的机器学习模型来填补这一空白。本研究的一个关键改进是将硬度作为一个特征变量纳入，从而提供了一个更强大的抗拉强度预测指标。改进后的模型在预测性能上有显著提升，XGBoost模型的R值达到了0.914。还应用了多项式回归来推导抗拉强度、合金成分和晶粒尺寸之间的数学关系，有助于更深入地理解这些相互依存关系。经模型提高的准确性验证，改进后的方法和分析技术不仅与铝合金相关，也有望应用于其他材料体系，可能会彻底改变材料性能的预测。
