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

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

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.

• Jezik: Angleški jezik
• Ključne besede: AA6082, hot extrusion, mechanical properties, yield strength, elongation, artificial neural networks, analysis
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: NTF - Naravoslovnotehniška fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2022
• Št. strani: 14 str.
• Številčenje: Vol. 12, iss. 3, art. 461
• PID: 20.500.12556/RUL-137540
• UDK: 669
• ISSN pri članku: 2075-4701
• DOI: 10.3390/met12030461
• COBISS.SI-ID: 100364035
• Datum objave v RUL: 21.06.2022

Gradivo je del revije

• Naslov: Metals
• Skrajšan naslov: Metals
• Založnik: MDPI AG
• ISSN: 2075-4701
• COBISS.SI-ID: 15976214

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 09.03.2022