Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082
In this paper, the Conditional Average Estimator artificial neural network (CAE ANN) was used to analyze the influence of chemical composition in conjunction with selected process parameters on the yield strength and elongation of an extruded 6082 aluminum alloy (AA6082) profile. Analysis focused on...
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MDPI AG
2022-03-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/3/461 |
| _version_ | 1797444965457985536 |
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| author | Iztok Peruš Goran Kugler Simon Malej Milan Terčelj |
| author_facet | Iztok Peruš Goran Kugler Simon Malej Milan Terčelj |
| author_sort | Iztok Peruš |
| collection | DOAJ |
| description | In this paper, the Conditional Average Estimator artificial neural network (CAE ANN) was used to analyze the influence of chemical composition in conjunction with selected process parameters on the yield strength and elongation of an extruded 6082 aluminum alloy (AA6082) profile. Analysis focused on the optimization of mechanical properties as a function of casting temperature, casting speed, addition rate of alloy wire, ram speed, extrusion ratio, and number of extrusion strands on one side, and different contents of chemical elements, i.e., Si, Mn, Mg, and Fe, on the other side. The obtained results revealed very complex non-linear relationships between all of these parameters. Using the proposed approach, it was possible to identify the combinations of chemical composition and process parameters as well as their values for a simultaneous increase of yield strength and elongation of extruded profiles. These results are a contribution of the presented study in comparison with published research results of similar studies in this field. Application of the proposed approach, either in the research and/or in industrial aluminum production, suggests a further increase in the relevant mechanical properties. |
| first_indexed | 2024-03-09T13:19:04Z |
| format | Article |
| id | doaj.art-0b33c86946f54c8a802561a3ca54ce4d |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T13:19:04Z |
| publishDate | 2022-03-01 |
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| record_format | Article |
| series | Metals |
| spelling | doaj.art-0b33c86946f54c8a802561a3ca54ce4d2023-11-30T21:31:36ZengMDPI AGMetals2075-47012022-03-0112346110.3390/met12030461Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082Iztok Peruš0Goran Kugler1Simon Malej2Milan Terčelj3Department for Materials and Metallurgy, Faculty for Natural Sciences and Engineering, University of Ljubljana, Aškerčeva 12, SI-1000 Ljubljana, SloveniaDepartment for Materials and Metallurgy, Faculty for Natural Sciences and Engineering, University of Ljubljana, Aškerčeva 12, SI-1000 Ljubljana, SloveniaDepartment for Physics and Chemistry of Materials, The Institute of Metals and Technology, Lepi pot 11, SI-1000 Ljubljana, SloveniaDepartment for Materials and Metallurgy, Faculty for Natural Sciences and Engineering, University of Ljubljana, Aškerčeva 12, SI-1000 Ljubljana, SloveniaIn this paper, the Conditional Average Estimator artificial neural network (CAE ANN) was used to analyze the influence of chemical composition in conjunction with selected process parameters on the yield strength and elongation of an extruded 6082 aluminum alloy (AA6082) profile. Analysis focused on the optimization of mechanical properties as a function of casting temperature, casting speed, addition rate of alloy wire, ram speed, extrusion ratio, and number of extrusion strands on one side, and different contents of chemical elements, i.e., Si, Mn, Mg, and Fe, on the other side. The obtained results revealed very complex non-linear relationships between all of these parameters. Using the proposed approach, it was possible to identify the combinations of chemical composition and process parameters as well as their values for a simultaneous increase of yield strength and elongation of extruded profiles. These results are a contribution of the presented study in comparison with published research results of similar studies in this field. Application of the proposed approach, either in the research and/or in industrial aluminum production, suggests a further increase in the relevant mechanical properties.https://www.mdpi.com/2075-4701/12/3/461AA6082hot extrusionmechanical propertiesyield strengthelongationartificial neural networks |
| spellingShingle | Iztok Peruš Goran Kugler Simon Malej Milan Terčelj Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082 Metals AA6082 hot extrusion mechanical properties yield strength elongation artificial neural networks |
| title | Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082 |
| title_full | Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082 |
| title_fullStr | Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082 |
| title_full_unstemmed | Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082 |
| title_short | Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082 |
| title_sort | contour maps for simultaneous increase in yield strength and elongation of hot extruded aluminum alloy 6082 |
| topic | AA6082 hot extrusion mechanical properties yield strength elongation artificial neural networks |
| url | https://www.mdpi.com/2075-4701/12/3/461 |
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