Modeling and prediction of surface roughness and dimensional accuracy in SLS 3D printing of PVA/CB composite using the central composite design

Abstract

Surface roughness and dimensional accuracy can be controlled by selecting the appropriate values for 3D printing variables. This paper investigates the effects of selective laser sintering (SLS) 3D printing main process variables and their interactions on surface roughness and dimensional accuracy. It also presents how to develop mathematical models to predict surface roughness and dimensional accuracy using a central composite design. It shows how to determine the appropriate values for the process variables to reach the desired surface roughness and dimensional accuracy separately and simultaneously. The analysis of variance is utilized to verify the adequacy of the developed models. Further, there is still a need for developing materials and composites for use with SLS technology. The combination of polyvinyl alcohol (PVA) and carbon black (CB) powder was used to print the PVA/CB composite specimens. In order to do this study, a desktop SLS 3D printer has been built. It is found that laser power, scan spacing, laser speed, and layer thickness significantly affect the surface roughness and dimensional accuracy. Also, the effects of interactions between process variables are considerable and should be determined in the surface roughness and dimensional accuracy models. The nonlinear behavior of some variables and their interactions on surface roughness are explained using the energy density plots. According to the results in the studied domains, reducing layer thickness, laser speed, and scan spacing tend to decrease the surface roughness. However, laser power reduction increases the surface roughness.