Exploring the Impact of Chemical Composition on the Oxidation Resistance of 2000 Series Aluminum Alloys using Extreme Vertices Design

Abstract

This research investigates optimizing the properties of 2024 aluminum alloy using the Extreme Vertices Design (EVD) method and linear regression. It examines the oxidation behavior of the alloy during solubilization heat treatment, specifically focusing on the effect of magnesium addition leading to a dark oxide layer. The study employs a comprehensive experimental design and regression models to estimate the specific oxidation rate constant (k). Analysis of results reveals variations in oxidation behavior among alloys and the influence of aluminum, copper, and magnesium concentrations on the oxidation rate. The regression analysis yields a comprehensive equation: k = -0.01Al - 4.06Cu - 15.71Mg + 4.52Al·Cu + 17.01Al·Mg + 418.5Cu:Mg - 447.7Al·Cu·Mg, with statistically significant results (p < 0.05) for all terms. An increase in magnesium concentration was found to enhance the oxidation rate, implying a higher alloy susceptibility to oxidation. These findings underline the value of the EVD method and regression analysis in alloy property optimization, thus aiding in the design of aluminum alloys with improved oxidation resistance.

Keywords

• 2024 aluminum alloy
• Mg content
• alloy optimization
• extreme vertices design
• oxidation behavior