Optimization of key quality indicators in material extrusion 3D printing of acrylonitrile butadiene styrene: The impact of critical process control parameters on the surface roughness, dimensional accuracy, and porosity

Additive Manufacturing (AM) has been increasingly used as a cost-effective manufacturing process, owing to its unique advantages, such as high flexibility and the unlimited capacity to fabricate complex geometries. The layer-by-layer of material extrusion (MEX) 3D printing process induces specific features to the produced parts, which highly affect their quality and may restrict their operating performance. Therefore, the optimization of the process parameters focused on the enhancement of external and internal key quality indicators, such as porosity, dimensional accuracy, and surface roughness, holds excessive research, technological and industrial merit. Herein, the effect of six (6) 3D printing control parameters, i.e., raster deposition angle, infill density, nozzle temperature, bed temperature, printing speed, and layer thickness, on the quality indicators of the 3D printed parts is investigated in depth. Optical Microscopy, Optical Profilometry, and Micro Χ-Ray Computed Tomography were employed to investigate and document these quality characteristics of MEX 3D printed test parts. To meet this goal, a massive and laborious experimental course, yielded through the Robust Design Theory, was performed. An L25 orthogonal array (25 runs) was compiled, for the six control parameters with five (5) levels for each of them. Hereto, with five replicas per experimental run, 125 samples were fabricated, whereas 500 experimental quality measurements were accomplished. The optimization quadratic regression models were then validated with five additional confirmation runs, i.e., 25 additional confirmation replicas and 100 confirmation quality measurements. For the first time, the surface quality features, as well as the geometrical and structural characteristics were investigated in such depth (>550 GB of raw experimental data were produced and processed). A thorough insight into the quality of the MEX 3D printed workpiece is provided allowing the control parameters’ ranking and optimization. Governing prediction equations yielding the quality features over the control parameters are introduced herein, holding a weighty industrial utility and merit.