Comparison of Artificial Neural Network and Multiple Regression Analysis Techniques in Predicting the Mechanical Properties of A3 56 Alloy

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 multi-layer ANN models were developed and trained using data from the literature and experimental results. A series of nonlinear regression models were also developed and the results were compared with the predictions made by the ANN models. 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. Unlike the nonlinear regression analysis, ANN can simplify the modelling process by eliminating the need to define a function. The results indicate that ANN is a suitable modelling technique for predicting mechanical properties of castings based on the alloy chemistry and processing variables.