Enhancing the mechanical properties of 3D-Printed polylactic acid through pellet additive manufacturing: A grey relational analysis based on entropy weights

The most prevalent and extensively employed additive manufacturing (AM) approach method is fused deposition modeling (FDM), which uses filament as feedstock. Pellet additive manufacturing (PAM) is an emerging technique within the field of FDM that utilizes thermoplastic pellets as the feedstock considering their greater ease of production compared to filaments. The PAM technique enables the production of intricate components with high dimensional precision and cost efficiency by eliminating the need to transform pellets into filaments. The discreet choice of printing parameters greatly influences the performance of 3D-printed objects. This work underscores the significance of printing parameters on mechanical performance measures, tensile, flexure, and hardness characteristics by utilizing a multi-objective optimization technique. It is a combination of the Taguchi, analysis of variance (ANOVA), and entropy-based grey relational analysis (EGRA). A Taguchi L9 orthogonal array is employed, with infill pattern, raster angle, and layer height as the control variables, while tensile and flexural strengths, and hardness serve as the output responses. The findings demonstrated that the optimum outcomes were achieved for the gyroid infill pattern at 45° orientation and 0.25 mm layer height. Enforcing EGRA in multi-objective optimization has resulted in an improvement of 3.3 % in the grey relational grade when compared to the initial parameter configurations. Hence, EGRA proves to be an effective potential tool for the optimization process in PAM.