PTFE in Wet and Dry Drilling: Two-Tier Modeling and Optimization through ANFIS

Abstract

This research is done to determine the optimum parameters to drill polytetrafluoroethylene (PTFE) and to investigate the effect of two-tier modeling for enhanced response in optimization. RSM model was done with L27 experimental design, considering speed (N), feed (f), and tool point angle (Ɵ). RSM data were further trained and tested using the Adaptive Neuro-Fuzzy Inference System (ANFIS), and β coefficient values were restructured to form revised RSM model. Both nonrevised RSM model and revised RSM model were used in Genetic Algorithm to locate the minimum surface roughness. ANFIS revised RSM model deviates from the experimental results by 2.6% and 2.86% for dry and wet condition; meanwhile, nonrevised RSM model deviates by 4.76% and 4.94%, respectively. The research concludes that two-tier modeling using RSM and ANFIS is better. Spindle speed of 1656 rpm, feed rate of 0.05 mm/min, and point angle of 100° are the optimum conditions to drill PTFE material where the best surface quality of 0.68 μm at wet drilling can be achieved.