Contour maps for simultaneous increase in yield strength and elongation of hot extruded aluminum alloy 6082

Peruš, Iztok (author), Kugler, Goran (author), Malej, Simon (author), Terčelj, Milan (author)

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
• UDC: 669
• ISSN on article: 2075-4701
• DOI: 10.3390/met12030461
• COBISS.SI-ID: 100364035
• Publication date in RUL: 21.06.2022

Record is a part of a journal

• Title: Metals
• Shortened title: Metals
• Publisher: MDPI AG
• ISSN: 2075-4701
• COBISS.SI-ID: 15976214

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