Correlation of Mechanical Properties of Cast Al 3xx Alloys to Processing Variables and Alloy Chemistry Using Regression Analysis and Artificial Neural Network Techniques

Abstract

The mechanical properties of aluminium alloy castings, such as EL%, YS and UTS, are controlled by the casting and heat treatment variables, alloy’s composition, and melt treatment. Despite the abundance of literature data, the large number of the controlling parameters has made it difficult to predict and model the mechanical properties by the conventional techniques. Another obstacle encountered when making such a prediction is the complex kinetics and interactions that exist among the many variables. The goal of this study was to develop Artificial Neural Network (ANN) and Multiple Regression models to predict the mechanical properties of A356 alloy from the processing variables. Several standard nonlinear regression and multi-layer ANN models were developed and trained using data from the literature and experimental results. Due to the complexity of A356’s solidification behaviour, the nonlinear regression produced results that were not as accurate as those produced by the ANN model. The results indicate that ANN is a suitable technique for predicting mechanical properties from alloy chemistry and processing variables.