| Autor: |
Shanxiang Wang, Zailiang Chen, Fei Qi, Chenghai Xu, Chunju Wang, Tao Chen, Hao Guo |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Fractal and Fractional, Vol 6, Iss 10, p 605 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2504-3110 |
| DOI: |
10.3390/fractalfract6100605 |
| Popis: |
The accurate characterization of the surface microstructure of ultra-high temperature ceramics after thermal shocks is of great practical significance for evaluating their thermal resistance properties. In this paper, a fractal reconstruction method for the surface image of Ultra-high temperature ceramics after repeated thermal shocks is proposed. The nonlinearity and spatial distribution characteristics of the oxidized surfaces of ceramics were extracted. A fractal convolutional neural network model based on deep learning was established to realize automatic recognition of the classification of thermal shock cycles of ultra-high temperature ceramics, obtaining a recognition accuracy of 93.74%. It provides a novel quantitative method for evaluating the surface character of ultra-high temperature ceramics, which contributes to understanding the influence of oxidation after thermal shocks. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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