Taguchi-assisted Multi-Objective Differential Evolution for enhancing mechanical performance of 3D printed PETG

This study presents a hybrid optimization approach combining the Taguchi design of experiments with Multi Objective Differential Evolution (MODE) to improve the mechanical performance of 3D printed polyethylene terephthalate glycol (PETG) components produced using Fused Deposition Modelling (FDM). The effects of four key process parameters, Infill Pattern (IP), Infill Density (ID), Layer Thickness (LT), and Nozzle Temperature (NT) were evaluated against three mechanical properties: Microhardness (HV), Impact Toughness (IT), and Ultimate Tensile Strength (UTS). Experimental data, generated using a Taguchi L16 orthogonal array, were analyzed through regression, signal-to-noise ratios, and MODE. The DE optimization achieved convergence in 52 generations, identifying an optimal parameter combination (IP = Grid, ID = 67.41%, LT = 0.1737 mm, NT = 240.51 °C) that delivered improved mechanical performance (HV = 16.5, IT = 1.01 J, UTS = 25.75 MPa). Comparative results demonstrate that MODE provides a systematic compromise solution for simultaneously considering HV, IT, and UTS within the tested design space, complementing response-wise Taguchi and regression analyses.