Abstract
Efficient removal of heat from the deformation zones in machining of difficult-to-cut materials is vital for attaining viability with respect to cost and productivity. The recently embraced heat removal and lubrication methods include applications of cryogenic fluids and minimum quantity of lubrication. This article presents an experimental investigation, complemented with a fuzzy modeling approach, for comparing the efficacies of using various combinations of CO2 snow and minimum quantity of lubrication in machining two tempers each of AISI 4340 and Ti-6Al-4V. In addition, cutting speed and feed rate are also included as predictor parameters, and their effects on tool damage, machining forces, and specific cutting energy consumption are evaluated. A total of 144 experimental runs are performed for developing the fuzzy knowledge-based model, and additional 20 experiments are conducted for testing its prediction accuracy. The model is also made capable of suggesting optimal settings of the cutting parameters and the most appropriate choice of cooling against various combinations of the objectives. In a nutshell, the cooling option of applying CO2 snow at the rake and flank faces of the tool proved beneficial for machining the titanium alloy while the option of using CO2 snow at the flank face and minimum quantity of lubrication at the rake face outshone the others in the case of the alloy steel. This article claims novelty with regard to machinability comparison of AISI 4340 and Ti-6Al-4V, application of cryogenic cooling to machining of hardened steels, investigation of hybrid cooling (CO2 snow plus minimum quantity of lubrication), and intelligent modeling of cryogenic machining of AISI 4340 and Ti-6Al-4V combined.