Effects of minimum quality lubrication with nano cutting fluid on surface milling of TI-6AL-4V alloy

Abstract

Surface Milling is used for making shapes, grooves and pockets in a workpiece surface. In this type of operations high surface quality is highly desirable. Tool life is another machinability index which is increased with decreasing tool wear. To reduce the cost of productivity it is badly needed to reduce the tool wear. The surface roughness and tool wear depend on several factors like cutting force, cutting temperature etc. Nano fluids has attracted a lot of attention to the researchers because of its excellent cooling and lubrication property. So in order to reduce the cutting temperature and cutting force in this thesis CNT-water soluble oil cutting fluids are used in minimum quality lubrication environment. As the milling operation is an intermittent cutting, the performance of separate mixing chamber for reducing the cutting zone temperature effectively was not reliable (because of intermittent flow). Although it is highly effective during the turning operation, because turning is a continuous cutting operation and intermittent flow do not affect the efficiency of the coolant. So, in this thesis an inline mixing chamber was developed which will ensure the continuous flow of coolant in the cutting zone. As a result of that the coolant can effectively penetrate the critical zones during milling operation and improve the surface quality and reduce the cutting force. The results of the inline mixing chamber were compared with the conventional flood cooling and it was observed that the inline mixing chamber under MQL environment was more effective in reducing average surface roughness and cutting force in milling. The cutting force and surface roughness value reduced 18 to 72% and 7 to 58% respectively under MQL environment. Finally, ANN model was developed for prediction of surface roughness as a function of cutting parameters and using RSM model the optimization of the cutting parameters was done with respect to the surface roughness. For the prediction, ANN showed 99.8 % accuracy.