Exploring the Relationship Between Tensile Strength and Alloy Composition

Aluminium alloys are used in a variety of applications and their tensile strength is a key property in determining their suitability for various engineering applications. Understanding the factors affecting tensile strength can help in designing more efficient alloys. In this study, a linear regression model is used to predict the tensile strength of aluminium alloys based on their chemical composition and processing conditions. A large number of different types of aluminium alloys were used in the experiments while two algorithmic linear regression models were developed and the model with accurate predictions was used as the result. The models reveal significant predictors between alloying elements and processing parameters, thus providing insights into optimising alloy properties and providing directions for future research. The final linear regression model developed experimentally successfully predicted the properties of aluminium alloys. The results of the study show that different compositional contents and processing temperatures in aluminium alloys also affect the tensile strength. This model found that different metal contents in different aluminium alloys and processing temperatures affect the tensile properties of the final alloy.