Automated post-processing of 3D-printed parts: artificial powdering for deep classification and localisation

Abstract

With the rapid rise in popularity in additive manufacturing, 3D printing technologies are increasingly competitive. However, current post-processing solutions are unable to cope with the varieties of printed parts, where post-production tasks differs for each part based on the desired outcome. Therefore, current post-processing treatments rely heavily on manual labor. Thus, to move towards end-to-end automated post-processing that can cater to individual processes for different parts, a fully automated vision pipeline for classifying and locating parts printed by polymer or metal powder-based processes, such as Selective Laser Sintering (SLS), binder jetting and HP Multi Jet Fusion (MJF) is proposed. The main contributions of this paper are the simulation of powder distribution on the surface of the 3D-printed parts after powder-based printing, and the incorporation of these powdered models in a sim-to-real deep learning pipeline. Our network that was trained on powdered models obtained classification and localisation results comparable to a network trained on real images. Also, the superiority of artificial powder models was shown when compared with using the original, unpowdered CAD models, especially for parts that are significantly different from their CAD models due to powder accumulation.

Keywords

• sim-to-real deep learning
• artificial powder generation
• object classification