Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys
| Autor: | Ana María Camacho, Alvaro Rodríguez-Prieto, David Merayo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Brinell hardness
yield strength Computer science Automotive industry Mechanical engineering 02 engineering and technology lcsh:Technology 01 natural sciences Article UTS Brinell scale 0103 physical sciences Ultimate tensile strength chemical composition Formability General Materials Science lcsh:Microscopy Aerospace lcsh:QC120-168.85 010302 applied physics lcsh:QH201-278.5 Artificial neural network lcsh:T heat treatment business.industry 021001 nanoscience & nanotechnology material properties’ prognosis lcsh:TA1-2040 aluminum Prognostics lcsh:Descriptive and experimental mechanics lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:Engineering (General). Civil engineering (General) 0210 nano-technology Material properties business lcsh:TK1-9971 artificial neural network |
| Zdroj: | Materials Volume 13 Issue 22 Materials, Vol 13, Iss 5227, p 5227 (2020) |
| ISSN: | 1996-1944 |
| DOI: | 10.3390/ma13225227 |
| Popis: | In metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%. |
| Databáze: | OpenAIRE |
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