Microstructure Control in 3D Printing with Digital Light Processing

Abstract

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.

Computing the Bidirectional Scattering of a Microstructure Using Scalar Diffraction Theory and Path Tracing

“…We first investigate the energy loss in single scattering on a slowly varying random surface of about 20 by 20 μ m 2 with a root mean square height of S q = 132 nm (Figure 6). The height variation was generated using sparse convolution noise [FW07; LFD∗20], which we also use when adding ground noise to a modeled microgeometry. We use to denote the amplitude of ground noise added to a nonplanar surface, as it is then no longer the actual root mean square height of the microgeometry.…”

“…As also seen in Figure 7, which compares the appearance rendered by each model using the scene for perceptual evaluation of BRDFs suggested by Havran et al [HFM16], the effect on the final appearance is significant. We further study this microgeometry by adding ground noise [FW07; LFD∗20] to the base geometry. The results are in Figures 8 and 9 and show that our model is able to deal with features at both nanoscale (where diffraction dominates) and microscale (where shadowing/masking and multiple scattering dominate).…”

“…Information about surface microgeometry is more and more commonly available. We can measure it with a pro‐filometer [DWMG15] or model it based on a desired surface structure [WDR11; MMRO13; LFD∗17; AHB18; LFD∗20]. It is even an option to find an explicit microgeometry representative of a normal distribution function [RBSM19].…”

Towards spatially varying gloss reproduction for 3D printing

“…By varying the order of inks, drying time, and varnish coverage, they were able to introduce a roughness to the surface, which resulted in different gloss levels. The idea can be abstracted to stereolitography where the surface roughness can be achieved by using sub-voxel growth of the printing resin [Luongo et al 2019]. Unfortunately, achieving matte finish using such techniques with a glossy varnish is challenging [Samadzadegan et al 2015].…”

Comparative Study on Polarization Spectral Parameters of Fruit in Southern Xinjiang Based on Roujean Model

“…In the field of materials, Yang Peiyan and others applied it to study the bi-directional properties of PTFE sheet diluent, aiming at reducing the temperature of the object under direct sunlight without refrigeration and consuming power [1]; Inoue and others used it to evaluate the surface morphology of paper, aiming at explaining the glossiness phenomenon of paper [2]. In the industrial field, Luongo et al [3] used it in the research of three-dimensional printing technology to provide a tool for users to estimate the reflectivity of printer control surface; Zhang Yingluo et al [4] used it in the design of underwater laser detection system. In the military field, Melvin et al [5] applied it to remote detection of mines and improvised explosive devices.…”