| Issue |
E3S Web Conf.
Volume 209, 2020
ENERGY-21 – Sustainable Development & Smart Management
|
|
|---|---|---|
| Article Number | 03003 | |
| Number of page(s) | 5 | |
| Section | Session 2. Advanced Energy Technologies: Clean, Resource-Saving, and Renewable Energy | |
| DOI | https://doi.org/10.1051/e3sconf/202020903003 | |
| Published online | 23 November 2020 | |
Determining the Resource of Safe Operation for Objects by Images
1 Nizhny Novgorod state technical university n. a. R. E. Alekseev, Nizhniy Novgorod, Russia Federation
2 ANO HE “Russian New University”, Moscow, Russia Federation
3 ANO International Nuclear Safety Center, Moscow, Russia Federation
4 ANO “Scientific and Research Center for Information in Physics and Technique”, Nizhny Novgorod, Russia Federation
* Corresponding author: vyach.andreev@mail.ru
In this paper, a systematic study of the microstructure damage process of metals and alloys was carried out. The main elements of the microstructure surface image, as well as the rules for the formation and interaction of rough slip traces and cracks to determine the model of damage accumulation on the image of the microstructure surface under cyclic loading are determined. A classifier that allows to determine the number of loading cycles before a sample goes out of service is proposed. A modernized structure of the convolutional neural network was developed to classify images of the damaged microstructure of the metals and alloys surface. The proposed classifier for determining the number of loading cycles made it possible to achieve a classification accuracy of 78.43%.
Key words: surface images of metals and alloys / accumulation of damage / surface damage / neural networks / classification
© The Authors, published by EDP Sciences, 2020
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