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Contour maps for simultaneous increase in yield strength and elongation of hot extruded aluminum alloy 6082
ID Peruš, Iztok (Author), ID Kugler, Goran (Author), ID Malej, Simon (Author), ID Terčelj, Milan (Author)

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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.

Language:English
Keywords:AA6082, hot extrusion, mechanical properties, yield strength, elongation, artificial neural networks, analysis
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:NTF - Faculty of Natural Sciences and Engineering
Publication status:Published
Publication version:Version of Record
Year:2022
Number of pages:14 str.
Numbering:Vol. 12, iss. 3, art. 461
PID:20.500.12556/RUL-137540 This link opens in a new window
UDC:669
ISSN on article:2075-4701
DOI:10.3390/met12030461 This link opens in a new window
COBISS.SI-ID:100364035 This link opens in a new window
Publication date in RUL:21.06.2022
Views:439
Downloads:84
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Record is a part of a journal

Title:Metals
Shortened title:Metals
Publisher:MDPI
ISSN:2075-4701
COBISS.SI-ID:15976214 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:09.03.2022

Projects

Funder:Other - Other funder or multiple funders
Funding programme:Republic of Slovenia, Ministry of Education, Science and Sport
Project number:OP20.03531

Funder:EC - European Commission
Funding programme:European Regional Development Fund

Funder:ARRS - Slovenian Research Agency
Project number:P2-0344
Name:Napredna metalurgija

Funder:ARRS - Slovenian Research Agency
Project number:P2-0268
Name:Geotehnologija

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