Deciphering the effect of grain boundary characteristics on fracture toughness of silicon nitride ceramics through a CNN regression model

Abstract

Fracture toughness of silicon nitride (Si3N4) ceramics with different grain boundary characteristics was determined from their microstructural images via a convolutional neural network (CNN) model. For this purpose, the CNN model was trained using Si3N4 ceramics with sintering additives of Y2O3 and MgO, and then the fracture toughness for those with various lanthanoid oxides and MgO was determined from the microstructural images through the trained model. Herein, the difference between the determined and measured values indicates the effect of the grain boundary characteristics because they little appear in the microstructures. The measured and AI-determined fracture toughness showed that the large ionic radius of Sm2O3 enhanced the fracture toughness of the Si3N4 ceramics sintered at 1850 °C for 24 h. The trained model successfully determined fracture toughness values that closely matched the measured ones from the microstructural images, even for samples with pores and different grain boundary characteristics, except for the Sm2O3 sample sintered for 24 h.