Examining and Modelling the Effects of 3D Fused Deposition Parameters
Abstract
Fused Deposition Modelling (FDM) is a prevalent method for rapid prototyping, offering flexibility and material efficiency. However, FDM-printed parts often lack desired mechanical properties, necessitating a thorough investigation of process parameters. This study explores the influence of FDM parameters on mechanical properties using Taguchi Design of Experiments (DOE) and Artificial Neural Networks (ANNs). The effects of layer thickness, raster angle, infill density, and nozzle temperature on mechanical characteristics were examined. Three Taguchi L9 experiments covered Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), and Chlorinated Polyethylene (CPE) materials, adhering to ASTM D638 standards. Results indicate a 90° raster angle improves strength by up to 47%, 52%, and 46% for PLA, ABS, and CPE, respectively, compared to 0°. Similarly, an 80% infill density enhances strength by over 27% for PLA, 30% for ABS, and 55% for CPE. A 45°/-45° raster angle significantly enhances ductility, improving by 47%, 80.5%, and 104% for PLA, ABS, and CPE, respectively. PLA exhibits superior tensile strength (57.6 MPa), while CPE shows higher ductility (maximum elongation of 0.0789%). ANN models predict strength and ductility with 97% and 94% accuracy, respectively.