EXPERIMENTAL INVESTIGATION ON MECHANICAL PROPERTIES OF FDM-BASED NYLON CARBON PARTS USING ANN APPROACH

Abstract

The implementation of the fused deposition modeling (FDM) technique in the production system is mainly due to its flexibility and ability to fabricate complex 3D prototypes and geometries. However, the mechanical strength of the printed parts needs to be investigated which was influenced by the process parameters such as layer thickness (LT), raster angle (RA), and Infill Density (ID). Therefore, these process parameters need to be optimized to attain better mechanical strength from the FDM printed parts. In this research, ePA-CF filament material was used to fabricate the specimens based on the selected process parameters such as LT (0.07, 0.14, and 0.20[Formula: see text]mm), RA (0∘, 45∘, and 90∘) and ID (50%, 75%, and 100%). The artificial neural network (ANN) method was implemented to determine the influential printing process parameters. Tensile, flexural, and impact tests were considered as the response parameters based on the various combination of the input parameters. It was concluded that the printing of nylon carbon parts using [Formula: see text][Formula: see text]mm, [Formula: see text], [Formula: see text] retains improved tensile strength of 66 MPa, flexural strength of 87[Formula: see text]MPa and impact strength of 12.5[Formula: see text]KJ/m2. Further, the propagation of cracks and the mode of failure were examined using SEM fractography. These observations substantiate that the selection of an optimal combination of FDM parameters assists in enhancing the mechanical strength of the printed nylon carbon parts.