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机器学习辅助的尖晶石耐火材料力学性能多元素优化

Machine-Learning-Assisted Multi-Element Optimization of Mechanical Properties in Spinel Refractory Materials.

作者信息

Chen Zhiyuan, Yang Daoyuan, Li Xianghui, Li Jinfeng, Yuan Huiyu, Cui Junyan

机构信息

School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China.

出版信息

Materials (Basel). 2025 Apr 9;18(8):1719. doi: 10.3390/ma18081719.

DOI:10.3390/ma18081719
PMID:40333391
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12028997/
Abstract

Using machine learning models, this study innovatively introduces multi-element compositions to optimize the performance of spinel refractories. A total of 1120 spinel samples were fabricated at 1600 °C for 2 h, and an experimental database containing 112 data points was constructed. High-throughput performance predictions and experimental verifications were conducted, identifying the sample with the highest hardness, (AlFeZnMgMn)O (1770.6 ± 79.1 HV1, 3.35 times that of MgAlO), and the highest flexural strength, (AlCrZnMgMn)O (161.2 ± 9.7 MPa, 1.4 times that of MgAlO). Further analysis of phase composition and microstructure shows that the mechanism of hardness enhancement is mainly the solid solution strengthening of multi-element doping, the energy dissipation of the large-grain layered structure, and the reinforcement of the zigzag grain boundary. In addition to solid solution strengthening and a compact low-pore structure, the mechanism of improving bending strength also includes second-phase strengthening and phase concentration gradient distribution. This method provides a promising way to optimize the performance of refractory materials.

摘要

本研究利用机器学习模型，创新性地引入多元素组成来优化尖晶石耐火材料的性能。共在1600℃下制备2小时1120个尖晶石样品，并构建了一个包含112个数据点的实验数据库。进行了高通量性能预测和实验验证，确定了硬度最高的样品，(AlFeZnMgMn)O（1770.6±79.1 HV1，是MgAlO的3.35倍），以及抗弯强度最高的样品，(AlCrZnMgMn)O（161.2±9.7 MPa，是MgAlO的1.4倍）。对相组成和微观结构的进一步分析表明，硬度增强的机制主要是多元素掺杂的固溶强化、大晶粒层状结构的能量耗散以及锯齿状晶界的强化。除了固溶强化和致密的低孔结构外，提高抗弯强度的机制还包括第二相强化和相浓度梯度分布。该方法为优化耐火材料性能提供了一条有前景的途径。

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