Parametric optimization of CNC turning on glass-fibre-reinforced plastic (GFRP) pipes: A grey-fuzzy logic approach

Abstract

Glass-fibre-reinforced plastic (GFRP) is an advanced polymeric glass fibre reinforced composite material being widely used in various applications such as aircrafts, robots and machine tools. An attempt was made by the past researchers on optimizing the cutting parameters of CNC turning on these filament wounded GFRG pipes with coated carbide tool inserts (K20 grade) as cutting tool by desirability function analysis using Taguchi technique. Machining process parameters such as cutting velocity, feed rate and depth of cut are optimized, while response parameters considered are surface roughness, flank wear, crater wear and machining force respectively. In this paper, grey relational analysis (GRA) combined with fuzzy logic is applied to this multi-objective optimization problem and the derived parametric mix is compared to that obtained by the past researchers. The predicted results of the parametric mix obtained using the proposed approach shows a significant improvement of approximately 14 % in the quality of response parameters as compared to that of the past researchers. Lastly, ANOVA is applied so as to identify the significant factors which positively contribute to the cutting process.