Surface roughness is gaining increasing recognition in the processing design methods of additive manufacturing (AM) due to its role in many critical applications. This impact extends not only to various AM product manufacturing but also to indirect applications, such as molding and casting. This review article discusses the role of processing on the surface roughness of AM-printed polymers with limited post-processing by summarizing recent advances. This review offers a benchmark for surface quality improvement of AM processes, considering the surface roughness of polymeric parts. For this purpose, it lists and analyzes the key processes and various printing parameters used to monitor and adjust surface roughness under given constraints. Four AM techniques for manufacturing polymeric parts are compared: fused filament fabrication (FFF), selective laser sintering (SLS), vat photopolymerization (VPP), and material jetting (MJT). A review and discussion of recent studies are presented, along with the most critical process parameters that affect surface roughness for the selected AM techniques. To assist in selecting the most appropriate method of 3D printing, comparable research summaries are presented. The outcome is a detailed survey of current techniques, process parameters, roughness ranges, and their applicability in achieving surface quality improvement in as-printed polymers.

Selective laser sintering (SLS) is a well-established technology that is used for additive manufacturing. Significant efforts have been made to improve SLS by optimizing the powder deposition, laser beam parameters, and temperature settings. The purpose is to ensure homogeneous sintering and prevent geometric and appearance inaccuracies in the manufactured objects. We evaluated the differences in the surface roughness and grain size of curved objects manufactured by using upcoming SLS technology that features two CO laser sources. Our analysis was carried out on polyamide 11 (PA11), which is a sustainable biobased polymer that has been gaining popularity due to its high-performance properties: its low melting point, high viscosity, and excellent mechanical properties. By using a Taguchi experimental design and analysis of variance (ANOVA), we examined the influence on the surface roughness and grain size of the build setup, the presence of thin walls, and the position of the sample on the powder bed. We found significant differences in some surface roughness and grain size measurements when these parameters were changed.

Anisotropy reveals interesting details of the subsurface structure of a material. We aim at noninvasive assessment of material anisotropy using as few measurements as possible. To this end, we evaluate different methods for detecting anisotropy when observing (1) several sample rotations, (2) two perpendicular planes of incidence, and (3) just one observation. We estimate anisotropy by fitting ellipses to diffuse reflectance isocontours, and we assess the robustness of this method as we reduce the number of observations. In addition, to support the validity of our ellipse fitting method, we propose a machine learning model for estimating material anisotropy.