Optimizing and predicting surface roughness on milling machine by using taguchi & Ann on D2 steel

Optimization means to find best value of input. For this Taguchi method gives the best output result. ANOVA is the most effecting parameter. This parameter affects the most among all the input parameters. Modeling is done by ANN. It is best network model which gives minimum error between network output and real output. Prediction is done by both methods. Result of both methods is compared. Best method is used which gives the minimum error. Milling process is used here because milling is most commonly used in industries. It can perform many tasks. D2 steel is used here. Various parameter are considered in this are taken from the previous papers. Taguchi and ANN methods are used because these are the most famous and developing methods. Taguchi method is used for optimization. It is done by studying the mathematical relation between input and output. Signal to noise ratio is basically considered in the optimization process. Signal should be large and noise should be less. ANN method is used for prediction. It shows that for any value of input data we can easily predict the output with minimum error.