An Integrated Approach of Particle Swarm Optimization and Grey Relational Analysis in Multi-Response Optimization of Fused Deposition Modeling

Abstract

This study proposes an integrated approach combining grey relational analysis (GRA) and particle swarm optimization (PSO) to optimize process parameters for fused deposition modeling (FDM) 3D printing using polylactic acid (PLA) material. Experimental design based on definitive screening designs (DSD) is employed to identify optimal printing parameters, focusing on improving surface finish, dimensional accuracy, and impact strength. A regression model, generated based on DSD, accurately predicts grey relational grades (GRG), facilitating efficient optimization. The model's effectiveness is validated through evaluation metrics and close agreement between actual and predicted GRG values. PSO further refines the optimization process by efficiently navigating the solution space towards superior printing parameters. A comparison between GRA and PSO reveals refinements in printing speed, indicating the more refined solutions by PSO. These findings highlight the effectiveness of the integrated approach in enhancing additive manufacturing performance.