微观结构
断裂韧性
材料科学
陶瓷
氮化硅
复合材料
卷积神经网络
计算机科学
人工智能
图层(电子)
作者
Ryoichi Furushima,Yutaka Maruyama,Y. Nakashima,Minh Chu Ngo,Tatsuki Ohji,Manabu Fukushima
摘要
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.
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