| Autor: |
Vitalii Emelianov, Anton Zhilenkov, Sergei Chernyi, Anton Zinchenko, Elena Zinchenko |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Heliyon, Vol 8, Iss 8, Pp e10002- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2405-8440 |
| DOI: |
10.1016/j.heliyon.2022.e10002 |
| Popis: |
The necessity to improve the metallographic analysis systems to automate diagnostics of the condition of the metals for all their characteristics has been substantiated. The metallographic analysis algorithm based on the use of neural networks for recognizing metal microstructures and a case-based reasoning approach for determining the metal grade is proposed. The structure of a multilayer neural network to determine the metals quantitative parameters has been developed. The recognizing results by neural networks for determining the metal quantitative parameters are shown. The high accuracy of determining the metals quantitative parameters by neural networks is presented. The specialized metallographic software to automate the recognition of metal microstructures and to determine the metal grade has been developed. Comparative results of carrying out metallographic studies with the developed neural network software to determine the metals quantitative parameters are shown. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
