Comprehensive analysis of drilling responses in additively manufactured PLA using a regression—based statistical learning approach

Abstract This study investigates the drilling characteristics of 3D-printed polylactic acid (PLA) materials, focusing on thrust force, torque, surface roughness, delamination factor, and chip formation under varying machining parameters. The experiments utilized a factorial design, varying in-fill densities (30%, 60%, and 90%), nozzle diameters (0.4 mm, 0.8 mm, and 1.2 mm), layer heights (100 μm, 200 μm, and 300 μm), spindle speeds (1200 RPM, 1500 RPM, and 1800 RPM), feed rates (10 mm min −1 , 15 mm min −1 , and 20 mm min −1 ), and drill diameters (4 mm, 6 mm, and 8 mm). The findings revealed that the thrust force ranged from 2.10 N to 6.54 N, while torque values varied between 0.12 Nm and 0.45 Nm, indicating a clear influence of the machining parameters on cutting performance. Surface roughness measurements demonstrated values from 0.18 μm to 0.76 μm, with lower in-fill densities and finer nozzle diameters contributing to smoother surfaces. The delamination factor increased from 1.03 to 1.16 as in-fill density rose from 30% to 90%. Chip analysis revealed three distinct types: long chips (5–6 cm), medium chips (1–2 cm), and fine chips (0.01–0.1 cm), correlated with specific parameter settings. The study highlights the significance of optimizing machining parameters, identifying an optimal combination of 30% in-fill density, 0.4 mm nozzle diameter, 300 μm layer height, 1800 RPM spindle speed, 10 mm min −1 feed rate, and 4 mm drill diameter to minimize delamination. The regression coefficients from response surface methodology suggest a predictive equation for the delamination factor, demonstrating an average prediction error of 3.17%. These findings contribute to enhanced understanding and optimization of drilling processes in 3D-printed PLA components, paving the way for improved quality and efficiency in manufacturing applications.