The focus of this study is on a new approach for determination of the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling Neural Network (NN) and Harmony Search (HS) algorithm. In this regard, advantages of statistical experimental design technique, experimental measurements, artificial neural network and Harmony Search algorithm were exploited in an integrated manner. For this purpose, numerous experiments on X20Cr13 stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness was created using a feed forward artificial neural network exploiting experimental data. The optimization problem was solved by Harmony Search algorithm. Additional experiments were performed to validate optimum surface roughness value predicted by HS algorithm. From the obtained results, it is clearly seen that the Harmony Search algorithm is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
An Improved Artificial Neural Network Design for Face Recognition utilizing Harmony Search Algorithm
