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

Zhiyuan Chen1, Daoyuan Yang1, Xianghui Li1

  • 1School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China.

Materials (Basel, Switzerland)
|May 7, 2025
PubMed
Summary

Machine learning optimized multi-element spinel refractories for enhanced performance. This research identified superior hardness and flexural strength in novel compositions, offering a new path for advanced refractory materials.

Keywords:
flexural strengthmachine learning modelsmicrohardnessmulti-element optimizationspinel refractory materials

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Area of Science:

  • Materials Science
  • Ceramics Engineering
  • Computational Materials Science

Background:

  • Spinel refractories are crucial in high-temperature industrial applications.
  • Optimizing spinel performance requires understanding complex compositional effects.
  • Existing methods for material optimization are often time-consuming and empirical.

Purpose of the Study:

  • To leverage machine learning for optimizing multi-element compositions in spinel refractories.
  • To identify spinel compositions with superior hardness and flexural strength.
  • To elucidate the microstructural mechanisms behind enhanced refractory properties.

Main Methods:

  • Fabrication of 1120 spinel samples at 1600 °C.
  • Construction of an experimental database with 112 data points.
  • Application of machine learning models for high-throughput performance prediction and experimental verification.

Main Results:

  • Identified (Al2Fe0.25Zn0.25Mg0.25Mn0.25)O4 with highest hardness (1770.6 ± 79.1 HV1).
  • Identified (Al2Cr0.5Zn0.1Mg0.2Mn0.2)O4 with highest flexural strength (161.2 ± 9.7 MPa).
  • Determined mechanisms including solid solution strengthening, layered structures, and grain boundary reinforcement.

Conclusions:

  • Multi-element doping significantly enhances spinel refractory hardness and strength.
  • Microstructural analysis reveals key strengthening mechanisms.
  • This machine learning-driven approach offers a promising strategy for optimizing refractory materials.