| Title: | Contour maps for simultaneous increase in yield strength and elongation of hot extruded aluminum alloy 6082 |
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| Authors: | ID Peruš, Iztok (Author)
ID Kugler, Goran (Author)
ID Malej, Simon (Author)
ID Terčelj, Milan (Author) |
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| Files: |  metals-12-00461.pdf (4,40 MB)
MD5: 2C457CE91F82110A47F3285D4DAD5EF6
 https://www.mdpi.com/2075-4701/12/3/461
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
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| Abstract: | 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. |
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| Keywords: | AA6082, hot extrusion, mechanical properties, yield strength, elongation, artificial neural networks, analysis |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 28.01.2022 |
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| Article acceptance date: | 07.03.2022 |
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| Publication date: | 09.03.2022 |
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| Publisher: | MDPI AG |
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| Year of publishing: | 2022 |
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| Number of pages: | str. 1-14 |
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| Numbering: | Vol. 12, iss. 3 |
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| PID: | 20.500.12556/DKUM-92037  |
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| UDC: | 669 |
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| ISSN on article: | 2075-4701 |
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| COBISS.SI-ID: | 100364035 |
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| DOI: | 10.3390/met12030461 |
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| Copyright: | © 2022 by the authors |
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| Publication date in DKUM: | 12.03.2025 |
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| Views: | 0 |
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| Downloads: | 0 |
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| Metadata: |    |
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| Categories: | Misc.
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