Prediction of bending strength of Si3N4 using machine learning

Abstract The bending strength of silicon nitride (Si3N4) plays a vital role in its application and is influenced by various process factors. Current experimental methods for investigating Si3N4 ceramics exhibiting low efficiency and high cost are incapable of systematically analysing the effect of process factors on the bending strength of Si3N4 ceramics and quantitatively predicting the optimum process parameters. In this study, machine learning (ML) approaches based on extreme gradient boosting (XGBoost) were applied to predict and analyse the bending strength of Si3N4 ceramics. Because the classification model of XGBoost is easily interpretable, the factors affecting the bending strength could be quantitatively evaluated. The current model can provide a suitable order of adding sintering additives to obtain excellent bending strength in Si3N4 ceramics. Although this study focuses on the bending strength of Si3N4 ceramics, the new approach reported herein is applicable for the in silico design and analysis of other ceramic materials.