SURFACE ROUGHNESS PREDICTION USING HYBRID NEURAL NETWORKS

Surface roughness is an important outcome in the machining process and it forms a major part in the manufacturing system. Surface roughness depends on different machining parameters and its prediction and control is a challenge to the researchers. There is a need to predict surface roughness prior to machining to attain higher productivity levels. Owing to advances in computing power there is an increase in the demand for the use of intelligent techniques. Recent research is directed towards hybridization of intelligent techniques to make the best out of each technique. This article proposes the development of a novel hybrid Neural Network (NN) trained with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the prediction of surface roughness. The proposed hybrid neural network is found to be competent in terms of computational speed and efficiency over the neural network model.