Predicting hardness of graphene-added Si3N4 using machine learning: A data-driven approach

This study presents a data-driven framework based on machine learning (ML) using extreme gradient boosting (XGBoost) for predicting the hardness of silicon nitride (Si₃N₄) ceramics reinforced with graphene. The XGBoost model takes into account various factors such as graphene type and content, characteristics of the raw Si₃N₄ powder, the parameters of the sintering process (sintering technique, temperature, pressure, holding time), and the characteristics of the sintered samples, i.e., the density, $\frac{\alpha }{\beta }$ content and Vickers hardness. The parameters that influence the Si₃N₄ hardness most strongly are identified, with sintering pressure, sintering time and density being the most influential. The addition of graphene content up to a certain threshold (1 wt%) has a positive impact on hardness. However, beyond that it leads to a lower density and a lower mechanical performance. Sintering parameters, particularly the sintering pressure, temperature, holding time and technique, strongly affect the density, final grain size, $\frac{\alpha }{\beta }$ Si₃N₄ composition and subsequently the hardness. The study highlights the importance of density and the densification process in achieving high hardness in Si₃N₄ ceramics. The developed ML model provides a valuable tool for predicting the hardness of Si₃N₄+graphene ceramics composites and offers insights into selecting suitable graphene type, content, and processing parameters. While the study primarily focuses on Si₃N₄+graphene composites, this novel approach holds promise for the in-silico design and analysis of diverse ceramic materials.