| Autor: |
Cristiano FRAGASSA, Matej BABIC, Ana PAVLOVIC |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Proceedings on Engineering Sciences, Vol 1, Iss 1, Pp 418-422 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2683-4111 |
| DOI: |
10.24874/PES01.01.054 |
| Popis: |
The accurate prediction of the mechanical properties of foundry alloys is a rather complex charge given the substantial variability of metallurgical conditions that can be created during casting even in the presence of minimal variations in the constituents and in the process parameters. In this study an application of intelligent methods, based on the machine learning, to the estimation of the hardness of a traditional spheroidal cast iron and a less common compact graphite cast iron is proposed. Microstructures are used as inputs to train the neural networks, while hardness is obtained as outputs. As general result, it is possible to admit that ‘light’ open source self-learning algorithms, combined with databases consisting of about 20-30 measures are already able to predict hardness properties with errors below 15%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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