Modelling and Optimization of Multiple Process Attributes of Electrodischarge Machining Process by Using a New Hybrid Approach of Neuro–Grey Modeling

Abstract

In the present article, a new hybrid approach of neuro-grey modeling (NGM) technique has been proposed for modeling and optimization of multiple process attributes of the electro discharge machining (EDM) process. It is proposed to simulate through an artificial neural network (ANN) for characterization of multiple process attributes followed by multiple process attributes optimization by using grey relational analysis (GRA) technique. A multineuron ANN of logistic sigmoid activation function has been designed. Levenberg–Marquardt algorithm involving second order error optimization has been chosen for training of the ANN because of its inherent merits. Then, using grey relational analysis (GRA) technique, a grey relational grade has been determined, which effectively represents the aggregate of different process attributes. As a result, a multi-attribute optimization can be converted into optimization of a single grey relational grade. The ANN is simulated first to characterize surface roughness (Ra), depth of heat-affected zone, microhardness value of machined surface, and material removal rate (MRR) with respect to current and pulse duration. Then, optimal values of current and pulse duration have been obtained. The NGM technique is found to be better and easy to implement.