Optimization of input parameters during drilling of glass fiber reinforced polymer composite using grey relational analysis

Glass fiber-reinforced polymer (GFRP) composites are extensively used in many sectors because of their high strength, low weight, and resistance to corrosion properties. Drilling operations are needed to make holes for the joining of the components. Various defects were reported during the drilling, and the authors in this study tried to provide an approach to minimize the drilling defects. Selection of the optimum level of the input parameters can reduce defects generated during drilling. This study used grey relational analysis to optimize input parameters during glass fiber-reinforced composite drilling. Three tools (T1, T2, T3) with point angles (118 degrees, 135 degrees, 140 degrees), feed rate in mm/min (10, 20, 30), and spindle speed in rpm (1000, 2000, 3000) were taken as input parameters. Taguchi L27 orthogonal arrays were selected for the three factors at three levels. Machining force (Fm), surface roughness (Ra), peel-up delamination factor (DF1), push-out delamination factor (DF2), hole size error, and circularity error were taken as output responses. Drilling GFRP composite by considering these input parameters combined with all these output responses using grey relational analysis was not explored before. The effect of input parameters on individual output response was analyzed using Taguchi and analysis of variance. Feed rate influenced machining force the most (65.34%), tool geometry influenced surface roughness the most (38.32%), and spindle speed influenced the push-out delamination factor the most (18.11%). SEM analysis was done to explain the aftereffects of drilling.