Machine Learning-Assisted Multi-Property Prediction and Sintering Mechanism Exploration of Mullite-Corundum Ceramics

Mullite-corundum ceramics are pivotal in heat transfer pipelines and thermal energy storage systems due to their excellent mechanical properties, thermal stability, and chemical resistance. Establishing relationships and mechanisms through traditional experiments is time-consuming and labor-intensive. In this study, gradient boosting regression (GBR), random forest (RF), and artificial neural network (ANN) models were developed to predict essential properties such as apparent porosity, bulk density, water absorption, and flexural strength of mullite-corundum ceramics. The GBR model (R² 0.91-0.95) outperformed the RF and ANN models (R² 0.83-0.89 and 0.88-0.91, respectively) in accuracy. Feature importance and partial dependence analyses revealed that sintering temperature and K₂O (~0.25%) positively affected bulk density while negatively influencing apparent porosity and water absorption. Additionally, sintering temperature, additives, and Fe₂O₃ (optimal content ~5% and 1%, respectively) were positively related to flexural strength. This approach provided new insight into the relationships between feedstock compositions and sintering process parameters and ceramic properties, and it explored the possible mechanisms involved.