Comparative Study of ANN and ANFIS Prediction Models For Turning Process in Different Cooling and Lubricating Conditions

The most efficient way to reduce friction and heat generation at the cutting zone is to use advanced cooling and lubricating techniques. In this paper, an experimental study was performed to investigate the capabilities of conventional, minimal quantity lubrication (MQL) and high pressure cooling (HPC) in the turning operations. Process parameters (feed, cutting speed and depth of cut) are used as inputs to the developed artificial neural network (ANN) and the adaptive networks based fuzzy inference systems (ANFIS) model for prediction of cutting forces, tool life and surface roughness. Results obtained by the models have been compared for their prediction capability with the experimentally determined values and very good agreement with experimental results was observed.