ABC and GA Optimized NN to Model Resin Bonded Mould/Core Sand System: A Soft Computing-based Approach

Abstract

Resin bonded sand system is an emerging area, and it can be used to produce dimensionally accurate castings with good surface finish. In the present paper, experimental investigations are carried out on the resin bonded cores, to develop a non-linear mathematical model, using the concept of design of experiments. Subsequently, an artificial neural network (ANN) with four neurons each on input and output layers has been used to model the resin bonded sand system. It is important to note that the process parameters, such as percentage of resin, percentage of hardener, number of strokes and curing time are considered as inputs and the mechanical properties of the core, namely compression strength, tensile strength, shear strength and permeability are treated as the outputs of the network. It is to be noted that the performance of developed ANN depends on several factors of the network, such as type of transfer functions, coefficients of transfer functions, number of neurons in the hidden layer and connecting weights between different layers. In the present study, two population based search and optimization algorithms, namely genetic algorithm (GA) and artificial bee colony (ABC) are used for optimizing the parameters of ANN. It has been observed that both GA and ABC trained neural networks (that is, GA-NN and ABC-NN) are found to have good agreement with the experimental data and can be used effectively to model the resin bonded core sand system.