Modeling and optimization of flexural properties of FDM-processed PET-G specimens using RSM and GWO algorithm

Abstract

As in every manufacturing process, fused deposition modeling (FDM) is strongly related to its operational parameters. 3D-printed components are anisotropic and brittle and this imposes the need to investigate the effect of FDM-related parameters to improve functionality and strength. In this paper the flexural strength of polyethylene terephthalate glycol (PET-G) is studied by testing different levels for five important process-related parameters; the height of each layer, density of infill, angle of deposited material, printing speed and printing temperature. A response surface experiment with 27 runs was conducted to obtain results for flexural strength (MPa) and proceed with the examination of the effect of each parameter on the response using statistical analysis. The experiments were performed according to ASTM D790 standard. Experimental observations dealing with fructure mechanics and failure modes were recorded and analysed. Based on the analysis of variance (ANOVA) a full quadratic regression equation was generated and verified for its efficiency. Finally the model was implemented as an objective function for a modern population-based algorithm known as grey wolf algorithm (GWO). It was shown that the algorithm can suggest good combination for parameter settings to maintain good flexural strength with a gain close to 15% compared to the highest value obtained by the series of experiments conducted.