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

Abstract

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, αβ 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, αβ 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.