There is currently a strong need for functional polymer components with tailored mechanical properties. The performance of these materials in Material Extrusion (MEX) 3D printing can be enhanced through effective optimization of the parameter values. This is important for semi-crystalline high-performance polymers (HPPs), such as polyvinylidene fluoride (PVDF), because they are more sensitive to the 3D printing settings utilized. This investigation was designed to optimize the MEX printed tensile strength of PVDF through the optimization of six critical variables (five levels of each): Raster Deposition Angle, Nozzle Temperature, Print Speed, Infill Density, Layer Thickness, and Build Bed Temperature. Filaments were produced from the raw materials, and standard specimens were created for testing purposes. Thermal, morphological, and structural characteristics of each sample were studied. The L25 Taguchi design was used to analyze the impact of these parameters on strength (tensile and yield), modulus of elasticity, toughness, specimen weight, and tensile strength per weight. The properties were influenced the most by Raster deposition angle (Young’s modulus improved by ∼30%, other properties considerably improved, too). Infill density was found to be statistically insignificant within the highly dense 80-100% regime examined. Reduced quadratic regression model quantified main effects, enabling the development of predictive equations. Confirmation runs revealed less than 10% value deviation for the models. R 2 values remained around 70%, indicating an intrinsic process variability. This study provides a statistically sound framework to optimize the processing of PVDF in MEX 3D printing and thus contributes to the production of robust HPP components.
Stratakis et al. (2026) studied this question.