MODELING AND OPTIMIZATION OF MACHINING PERFORMANCE MEASURES IN FACE MILLING OF AUTOMOTIVE ALUMINUM ALLOY A380 UNDER DIFFERENT LUBRICATION/COOLING CONDITIONS FOR SUSTAINABLE MANUFACTURING

The use of cutting fluids in machining process is very essential for achieving desired machining performance. Due to the strict environmental protection laws now in effect, there is a wide-scale evaluation of the use of cutting fluids in machining. Consequently, minimal quantity lubrication (MQL), which uses very small quantity of cutting fluids and still offers the same functionality as flood cooling, can be considered as an alternative solution. This thesis presents an experimental study of face milling of automotive aluminum alloy A380 under four different lubrication/cooling conditions: dry cutting, flood cooling, MQL (Oil), and MQL (Water). Experiments were design using Taguchi method for design of experiments. Empirical models for predicting surface roughness and cutting forces were developed for these four conditions in terms of cutting speed, feed and depth of cut. Optimization technique using Genetic Algorithms (GA) was used to optimize performance measures under different lubrication/cooling conditions, based on a comprehensive optimization criterion integrating the effects of all major machining performance measures. Case studies are also presented for two pass face milling operation comparing flood cooling condition with MQL. The comparison of the results predicted by the models developed in this work shows that the cutting force for MQL (Oil) is either lower or equal to flood cooling. The surface roughness for MQL (Oil) is comparable to flood cooling for higher range of feed and depth of cut. A comparison of the optimized results from the case studies, based on value of utility function, shows that the optimum point for two pass face milling operation having MQL (Oil) as finish pass has highest utility function value