Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys
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 ne...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/13/22/5227 |
| _version_ | 1797547522955149312 |
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| author | David Merayo Alvaro Rodríguez-Prieto Ana María Camacho |
| author_facet | David Merayo Alvaro Rodríguez-Prieto Ana María Camacho |
| author_sort | David Merayo |
| collection | DOAJ |
| description | 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 <inline-formula><math display="inline"><semantics><mrow><mn>95</mn><mo>%</mo></mrow></semantics></math></inline-formula>. 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 <inline-formula><math display="inline"><semantics><mrow><mn>95</mn><mo>%</mo></mrow></semantics></math></inline-formula>. |
| first_indexed | 2024-03-10T14:45:15Z |
| format | Article |
| id | doaj.art-da6883c99892445aba8d9e8cfc42f208 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T14:45:15Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-da6883c99892445aba8d9e8cfc42f2082023-11-20T21:30:48ZengMDPI AGMaterials1996-19442020-11-011322522710.3390/ma13225227Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum AlloysDavid Merayo0Alvaro Rodríguez-Prieto1Ana María Camacho2Department of Manufacturing Engineering, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal 12, 28040 Madrid, SpainDepartment of Manufacturing Engineering, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal 12, 28040 Madrid, SpainDepartment of Manufacturing Engineering, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal 12, 28040 Madrid, SpainIn 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 <inline-formula><math display="inline"><semantics><mrow><mn>95</mn><mo>%</mo></mrow></semantics></math></inline-formula>. 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 <inline-formula><math display="inline"><semantics><mrow><mn>95</mn><mo>%</mo></mrow></semantics></math></inline-formula>.https://www.mdpi.com/1996-1944/13/22/5227aluminumartificial neural networkchemical compositionheat treatmentBrinell hardnessmaterial properties’ prognosis |
| spellingShingle | David Merayo Alvaro Rodríguez-Prieto Ana María Camacho Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys Materials aluminum artificial neural network chemical composition heat treatment Brinell hardness material properties’ prognosis |
| title | Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys |
| title_full | Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys |
| title_fullStr | Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys |
| title_full_unstemmed | Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys |
| title_short | Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys |
| title_sort | prediction of mechanical properties by artificial neural networks to characterize the plastic behavior of aluminum alloys |
| topic | aluminum artificial neural network chemical composition heat treatment Brinell hardness material properties’ prognosis |
| url | https://www.mdpi.com/1996-1944/13/22/5227 |
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