Multi-objective Optimization on Surface Roughness of 3D-Printed Parts by Fused Deposition Modelling

Abstract

Because of its capacity to make components of complicated designs with less production time and expense, additive manufacturing (AM) technologies like as fused deposition modelling (FDM) have been widely employed in today's manufacturing industries such as transportation, aerospace, and medical. However, in order to get the greatest quality of printed component, careful selection of input process parameters is critical. This study presents unique techniques for figuring out the appropriate parameter settings to enhance surface quality, or the surface roughness of FDM printed objects, such as the response surface methodology. The input variables were extrusion temperature, layer height, and printing speed, while the output response was the roughness of the surface (horizontal, vertical and inclined). The experiment was created using technique known as response surface methodology (RSM). Then, using a regression model, the link between the input parameters and the surface roughness was determined. The statistics show that the surface roughness obtained by RSM rose by 95.69% for horizontal surface, 97.86 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for vertical surface, and 97.56 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for inclined surface. The predicted surface roughness and the observed values also matched each other well.