Evolution of Artificial Neural Network (ANN) Model for Predicting Secondary Dendrite Arm Spacing in Aluminium Alloy Casting

Abstract

Extensive solidification simulations are conducted using finite difference method on an aluminium alloy casting. Orthogonal experimental array layout is considered for running experimental simulations. Microstructural parameter Secondary Dendrite Arm Spacing (SDAS) at three different locations was predicted as response variable, through solidification simulations by varying the process parameters. The input process variables are pouring temperature, insulation on riser and chill volume heat capacity. An Artificial Neural Network (ANN) model was developed to predict the response variable for varied input process variables. Through sensitivity analysis the influence of input process variables on output response was obtained. The results obtained from solidification simulations and ANN model are validated experimentally.