4.7 Article

Artificial Intelligence-based determination of fracture toughness and bending strength of silicon nitride ceramics

期刊

JOURNAL OF THE AMERICAN CERAMIC SOCIETY
卷 106, 期 8, 页码 4944-4954

出版社

WILEY
DOI: 10.1111/jace.19147

关键词

deep learning; fracture mechanics; toughness; microstructure; silicon nitride; strength

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The mechanical properties of silicon nitride ceramics, fracture toughness (K-IC) and bending strength (sigma), were determined using convolutional neural network (CNN) models based on microstructural images. A database of Si3N4 samples with various sintering additives and process conditions was used to train the CNN models and test their accuracy. The determination coefficients (R-2) were approximately 0.85 for K-IC and 0.92 for sigma, indicating high accuracy. The lower R-2 value for K-IC suggests that it is more influenced by microstructural information such as grain-boundary characteristics. The trained models correctly recognized the microstructural differences among the images to determine the mechanical properties.
Two mechanical properties, fracture toughness (K-IC) and bending strength (sigma), of silicon nitride (Si3N4) ceramics were determined from their microstructural images via convolutional neural network (CNN) models. The Si3N4 samples used for database were fabricated using various kinds of sintering additives under different process conditions. In total, 330 data sets were prepared and used for building the CNN models for artificial intelligence-bassed determination of the two mechanical properties and testing the determination accuracy of the trained models. The determination coefficients (R-2), which were used as accuracy indices, were approximately 0.85 for K-IC and 0.92 for sigma. Although both the R-2 values were relatively high, the lower value for K-IC suggests that it is influenced more by what is little obtained from the microstructural information, such as grain-boundary characteristics. Furthermore, gradient-weighted class activation mapping, which can visualize which parts of the image the CNN models focus on, showed that the trained models determined the two mechanical properties based on correct recognition of the microstructural difference among the images.

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