Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network

In this study, an artificial neural network (ANN) based predictive model of average surface roughness in turning hardened EN 24T steel has been presented. The prediction was performed by using Neural Network Tool Box 7 of MATLAB R2015a for different levels of cutting speed, feed rate, material hardness and cutting conditions. To be specific the dry and high pressure coolant (HPC) jet environments were explored as cutting conditions. The experimental runs were determined by full factorial design of experiment. Afterward the 3-n-1, 3-n-2 and 4-n-1 ANN architectures were trained by utilizing the Levenberg-Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms, and evaluated based on the lowest root mean square error (RMSE). The 3-10-1 and 3-4-2 ANN models, trained by BR, revealed the lowest RMSE. A good prediction fit of the models was established by the regression coefficients higher than 0.997. At last, the behavior of the surface roughness in respect of speed-feedhardness for dry and HPC conditions has been analyzed. The HPC reduced surface roughness by the efficient cooling and lubrication whereas the higher hardness of material induced higher average surface roughness due to higher restraining force against tool imposed cutting force. (C) 2016 Elsevier Ltd. All rights reserved.