Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network

The fracture toughness of silicon nitride (Si3N4) ceramics was evaluated directly from their microstructures via deep learning using convolutional neural network models. Totally 156 data sets containing microstructural images and relative densities were prepared from 45 types of Si3N4 samples as input feature quantities (IFQs) and were correlated to the fracture toughness as an objective variable. The data sets were divided into two groups. One was used for training, resulting in the creation of regression models for two kinds of IFQs: the microstructures only and a combination of the microstructures and the relative densities. The other group was used for testing the validity of the created models. As a result, the determination coefficient was approximately 0.8 even when using only the microstructures as the IFQs and was further improved when adding the relative densities. It was revealed that the fracture toughness of Si3N4 ceramics was well evaluated from their microstructures.

Automated summary

The fracture toughness of silicon nitride ceramics can be evaluated directly from their microstructures using deep learning and convolutional neural network models. The study found that even when using only the microstructures as features, the determination coefficient was approximately 0.8, and further improved when the relative densities were added, indicating that the microstructures can effectively assess the fracture toughness of silicon nitride ceramics.