LAUSR

Drilling is one of the indispensable material removal processes in practically all the manufacturing industries for generation of through/blind holes in solid materials. Based on a face-centred central composite design plan, this paper proposes parametric analysis and optimization of drilling of biodegradable 3D-printed polylactic acid (PLA) polymer fabricated through additive manufacturing process, and also endeavours to investigate influences of spindle speed (SS), feed rate (FR) and drill diameter (DD) on material removal rate (MRR), circularity (CIR), cylindricity (CYL), delamination factor (DF) and surface roughness (SR) of the drilled PLA components. It is observed through the main effects plots that higher SS would lead to increased MRR, and decreased DF, CIR, CYL and SR values. However, higher FR would result in increased MRR, DF and SR, and moderately lower CIR and CYL values. Similarly, higher MRR and SR can be attained at higher DD, whereas, moderately lower DF, CIR and CYL can be achieved at increasing values of DD. The said drilling operation on 3D-printed PLA polymer is optimized using grey wolf optimizer (GWO), resulting in identification of the ideal parametric combination as SS = 870 rpm, FR = 0.15 mm/rev and DD = 4 mm. At that combination, the corresponding response values are obtained as MRR = 0.024 g/min, DF = 1.015, CIR = 0.0188 mm, CYL = 0.037 mm and SR = 1.73 μm, when equal importance is allocated to all of them. Although an analysis of its performance against other state-of-the-art optimization algorithms, like artificial bee colony (ABC), ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA) and teaching learning-based optimization (TLBO) reveals almost equally comparable results, GWO outperforms ABC, ACO, PSO, GA and TLBO algorithms with respect to average computing time, saving 115.03%, 51.30%, 21.24%, 60.62% and 39.90% of the time, respectively.