Artificial evolutionary approaches to produce smoother surface in magnetic abrasive finishing of hardened AISI 52100 steel

Abstract

In this work, two models of feed forward back-propagation neural network (FFBP-NN) and adaptive neuro-fuzzy inference system (ANFIS) have been developed to predict the performance of magnetic abrasive finishing process, based on experimental data of literature [7]. Input parameters of process are electromagnet’s voltage, mesh number of abrasive particles, poles rotational speed and weight percent of abrasive particles, and also the output is percentage of surface roughness variation. In order to select the best model, a comparison between developed models has been done based on their mean absolute error (MAE) and root mean square error (RMSE). Moreover, optimization methods based on simulated annealing (SA) and particle swarm optimization (PSO) algorithms were used to maximize the percent of surface roughness variation and select the optimal process parameters. Results indicated that the models based on artificial intelligence predict much more precise values with respect to predictive regression model developed in main literature [7]. Also, the ANFIS model had a lowest value of MAE and RMSE with respect to others. So it was used as an objective function to maximize the surface roughness variation by using SA and PSO. Comparison between the obtained optimal solutions and analysis of results in main literature indicated that SA and PSO could find the optimal answers logically and precisely.