Optimization of Machining Parameters to Minimize Surface Roughness using Integrated ANN-GA Approach

The surface roughness is a widely used index of product quality in terms of precision fit of mating surfaces, fatigue life improvement, corrosion resistance, aesthetics, etc. Surface roughness also denotes the amount of energy and other resources consumed during machining. This paper presents an approach for determining the optimum machining parameters leading to minimum surface roughness by integrating Artificial Neural Network(ANN) and Genetic Algorithm (GA). To check the capability of the ANN-GA approach for prediction and optimization of surface roughness, a real machining experiment has been referred in this study. A feed forward neural network is developed by collecting the data obtained during the turning of Ti-6Al-4 V titanium alloy. The MATLAB toolbox has been used for training and testing of neural network model. The predicted results using ANN indicate good agreement between the predicted values and experimental values. Further, GA is integrated with neural network model to determine the optimal machining parameters leading to minimum surface roughness. The analysis of this study proves that the ANN-GA approach is capable of predicting the optimum machining parameters.