Taguchi Grey Relational Analysis for Multi-Objective FDM Parameter Optimization of PLA Components

Abstract

Fused Deposition Modelling (FDM) employs Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), and other materials to manufacture items from Computer Aided Design (CAD) files in recent era. Process parameter optimization could aid in producing durable products. This article presents multi-objective parametric optimization for the FDM process. The infill density, orientation angle, and layer height characteristics are studied in proposed work. In this task, PLA material is used to create FDM parts. Using Taguchi grey relational analysis, the printing time, surface roughness, dimensional accuracy, and tensile strength are optimised. Analyses of Variance (ANOVA) assesses the importance of process factors relative to response parameters. The recommended method aids decision analysts in comprehending the whole evaluation process and expedites the production of components with exceptional surface finish, dimensional accuracy, and tensile strength with optimum time. The layer height, orientation angle, and infill density have the most effects on surface roughness, according to the data. Finally, the results shows that the orientation angle, layer height, and infill density have the greatest effects on dimensional variance. Grey Relational Grade (GRG) was able to ascertain the ideal values of the parameters layer height (0.3 mm), orientation angle (90°), and infill density (40%) using the Grey Taguchi Method.