MODELING AND OPTIMIZATION OF FLANK WEAR AND SURFACE ROUGHNESS OF MONEL-400 DURING HOT TURNING USING ARTIFICIAL INTELLIGENCE TECHNIQUES

This work aims to model and investigate the effect of cutting speed, feed rate, depth of cut and the workpiece temperature on surface roughness and flank wear (responses) of Monel-400 during turning operation. It also aims to optimize the machining parameters of the above operation. A power-law model is developed for this purpose and is corroborated by comparing the results with the artificial neural network (ANN) model. Based on the coefficient of determination (R-2), mean square error (MSE), and mean absolute percentage error (MAPE) the results of the power-law model are found to be in close agreement with that of ANN. Also, the proposed power law and ANN models for surface roughness and flank wear are in close agreement with the experiment results. For the power- law model R-2, MSE, and MAPE were found to be 99.83%, 9.9x10(-4), and 3.32x10(-2), and that of ANN were found to be 99.91%, 5.4x10(-4), and 5.96x10(-2), respectively for surface roughness and flank wear. An error of 0.0642% (minimum) and 8.7346% (maximum) for surface roughness and 0.0261% (minimum) and 4.6073% (maximum) for flank wear were recorded between the observed and experimental results, respectively. In order to optimize the objective functions obtained from power-law models of the surface roughness and flank wear, GA (genetic algorithm) was used to determine the optimal values of the operating parameters and objective functions thereof. The optimal value of 2.1973 mu m and 0.256 mm were found for surface roughness and flank wear, respectively.