Constrained optimum surface roughness prediction in turning of X20Cr13 by coupling novel modified harmony search-based neural network and modified harmony search algorithm

Nowadays, manufacturers rely on trustworthy methods to predict the optimal cutting conditions which result in the best surface roughness with respect to the fact that some constraining functions should not exceed their critical values because of current restrictions considering competition found among them in delivering economical and high-quality products to the stringent customers in the shortest time. The present research deals with a modified optimization algorithm of harmony search (MHS) coupled with modified harmony search-based neural networks (MHSNN) to predict the cutting condition in longitudinal turning of X20Cr13 leading to optimum surface roughness. To this end, several experiments were carried out on X20Cr13 stainless steel to attain the required data for training of MHSNN. Feed-forward artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting experimental data, and the MHS algorithm was used to find the constrained optimum of surface roughness. Furthermore, simple HS algorithm was used to solve the same optimization problem to illustrate the capabilities of the MHS algorithm. The obtained results demonstrate that the MHS algorithm is more effective and authoritative in approaching the global solution compared with the HS algorithm.