Artificial Intelligence System Approach for Optimization of Drilling Parameters of Glass-Carbon Fiber/Polymer Composites

Abstract

In recent times, the study on machining characteristics of combined (hybrid) fiber polymer composites has drawn a remarkable research attention because of its emerging industrial applications. The present study focuses on the drilling of hybrid glass-carbon fiber reinforced (GCFR) epoxy composites fabricated using hand layup technique. The machining characteristics were considered in the drilling of GCFR composites which include thrust force, torque, delamination factor and surface roughness. The influence of the drilling process parameters such as spindle speed, drill diameter and feed rate on the characteristics are studied. To avoid the confounding effect of the individual optimized characteristics, an artificial intelligence system i.e. fuzzy inference system approach is adopted. The fuzzy inference system transformed all the performance characteristics of drilled hybrid composites into a multi response performance index (MPI) and optimized the MPI at the common factor level setting. The optimal combination of process parameters for minimum thrust force, torque, delamination factor and surface roughness found to be: speed 3000 RPM, drill diameter 5 mm and the feed rate 50 mm/min. The analysis of variance results show that drill diameter is the most significant parameter followed by feed rate and speed. Further, a theoretical model was proposed for the estimation of MPI and found that an average absolute error of 14.8% with respect to the experimental MPI data is obtained. © 2020, Springer Nature B.V.