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.

Digital light processing stereolithography is a promising technique for 3D printing. However, it offers little control over the surface appearance of the printed object. The printing process is typically layered, which leads to aliasing artefacts that affect surface appearance. An antialiasing option is to use greyscale pixel values in the layer images that we supply to the printer. This enables a kind of subvoxel growth control. We explore this concept and use it for editing surface microstructure. In other words, we modify the surface appearance of a printed object by applying a greyscale pattern to the surface voxels before sending the cross‐sectional layer images to the printer. We find that a smooth noise function is an excellent tool for varying surface roughness and for breaking the regularities that lead to aliasing. Conversely, we also present examples that introduce regularities to produce controlled anisotropic surface appearance. Our hope is that subvoxel growth control in stereolithography can lead 3D printing towards customizable surface appearance. The printing process adds what we call ground noise to the printed result. We suggest a way of modelling this ground noise to provide users with a tool for estimating a printer's ability to control surface reflectance.

Rendering translucent materials using Monte Carlo ray tracing is computationally expensive due to a large number of subsurface scattering events. Faster approaches are based on analytical models derived from diffusion theory. While such analytical models are efficient, they miss out on some translucency effects in the rendered result. We present an improved analytical model for subsurface scattering that captures translucency effects present in the reference solutions but remaining absent with existing models. The key difference is that our model is based on ray source diffusion, rather than point source diffusion. A ray source corresponds better to the light that refracts through the surface of a translucent material. Using this ray source, we are able to take the direction of the incident light ray and the direction toward the point of emergence into account. We use a dipole construction similar to that of the standard dipole model, but we now have positive and negative ray sources with a mirrored pair of directions. Our model is as computationally efficient as existing models while it includes single scattering without relying on a separate Monte Carlo simulation, and the rendered images are significantly closer to the references. Unlike some previous work, our model is fully analytic and requires no precomputation.

We tackle the problem of generating novel-view images from collections of 2D images showing refractive and reflective objects. Current solutions assume opaque or transparent light transport along straight paths following the emission-absorption model. Instead, we optimize for a field of 3D-varying index of refraction (IoR) and trace light through it that bends toward the spatial gradients of said IoR according to the laws of eikonal light transport.