Multi-objective optimization of 3D printed PLA/carbon fibre composite using a combined approach of gray relational analysis and ANOVA
Vishal Mishra, Nikhil Bharat, Vijay Kumar, Murugan Vellaisamy, Dhinakaran Veeman
Abstract
This study explores the material extrusion (MEX) based 3D printing of polylactic acid (PLA) reinforced with milled carbon fibre (MCF) composite using the fused deposition modeling (FDM) technique. A Taguchi L 16 orthogonal array design was employed to investigate the influence of key FDM process parameters (printing temperature (PT), layer thickness (LT), infill pattern (IP), and print speed (PS)) on the tensile properties of the composite, including Young’s modulus (E), maximum tensile load (L max ), ultimate tensile stress (UTS), and strain at maximum stress (∊). A combined approach of Gray Relational Analysis (GRA) and Analysis of Variance (ANOVA) was utilized to optimize the process parameters. The results indicate that the optimal combination of 210°C PT, 0.2 mm LT, rectilinear IP, and 30 mm/s PS achieved the highest Gray Relational Grade (GRG). ANOVA further revealed that print speed had the most significant impact on tensile properties (34.84%), followed by layer thickness (30.70%), infill pattern (23.92%), and printing temperature (1.40%). The developed linear model demonstrated high predictive accuracy, with an R 2 value of 90.96%. Optical microscopy analysis confirmed that FDM parameters significantly influenced the microstructural characteristics and fracture behavior of the composite. These findings highlight the potential of PLA-MCF composites for high-strength engineering applications, advancing additive manufacturing technologies in structural and functional components.