Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network
Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network
复制标题
通过卷积神经网络从微观结构评估氮化硅的断裂韧性
DOI：
10.1111/jace.18795
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发表时间：
2022
影响因子：
3.9
通讯作者：
M. Fukushima
中科院分区：
文献类型：
--
作者：
R. Furushima;Yutaka Maruyama;Yuki Nakashima;Minh Chu Ngo;T. Ohji;M. Fukushima
利用卷积神经网络模型，通过深度学习，直接从氮化硅(Si3N4)陶瓷的微观结构中估算其断裂韧性。从45种类型的Si3N4样品中制备了156个包含显微组织图像和相对密度的数据集作为输入特征量(IFQ)，并以断裂韧性为目标变量进行了关联。数据集被分成两组。一种用于训练，结果建立了两种IFQ的回归模型：只有微观结构和微观结构和相对密度的组合。另一组用于检验所创建模型的有效性。结果表明，即使仅用微观结构作为IFQs，测定系数也约为0.8，增加相对密度后，测定系数进一步提高。结果表明，Si3N4陶瓷的断裂韧性可以从微观结构上得到较好的评价。
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 Si3N4samples 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 Si3N4ceramics was well evaluated from their microstructures.