Grey Fuzzy Multiobjective Optimization of Process Parameters for CNC Turning of GFRP/Epoxy Composites

For obtaining close fits and tolerances, certain amount of machining has to be carried out on GFRP (Glass Fibre Reinforced Plastic) composites, produced by primary manufacturing processes. A number of cylindrical GFRP composite parts are finish machined by turning. These include axles, bearings, spindles, rolls and steering columns. There is always a tradeoff between quality and productivity during machining operations. Hence it becomes essential to evaluate the optimal cutting parameters setting in order to satisfy these opposing requirements. In this study, a hybrid multiobjective optimization algorithm involving grey and fuzzy coupled with Taguchi methodology is used. Four process parameters, each at three levels are selected for the study viz. cutting tool nose radius, cutting speed, feed rate and depth of cut. Surface roughness parameter Ra, tangential cutting force Fz and material removal rate MRR are the chosen output performance measures. The experimental plan is laid according to Taguchi's orthogonal array L27. Woven fabric based GFRP/ Epoxy tubes produced using hand layup process are finish turned using PCD cutting tool. Grey relational coefficients of the three performance measures are converted into a single multi performance characteristics index (MPCI) using Mamdani type fuzzy inference system. This MPCI is then optimized using Taguchi analysis. The parameter combination of A2B1C1D3, i.e. tool nose radius of 0.8 mm, cutting speed of 120 m/min, feed rate of 0.05 mm/rev and depth of cut of 1.6 mm, is evaluated as the optimum combination. The confirmatory experiment at these settings gave maximum value of MPCI, validating the results.