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.

摘要

随着增材制造的迅速普及，3D打印技术变得越来越有竞争力。但是，当前的后处理解决方案无法应付各种印刷零件，其中，根据所需结果，每个零件的后生产任务都不同。因此，当前的后处理方法严重依赖体力劳动。因此，为了朝着可以满足不同零件的单个工艺的端到端自动化后处理的方向发展，一个全自动的视觉管线用于对基于聚合物或金属粉末的工艺（例如选择性激光烧结）印刷的零件进行分类和定位（ SLS），粘合剂喷射和HP Multi Jet Fusion（MJF）。本文的主要贡献是在粉末基打印后模拟3D打印零件表面上的粉末分布，并将这些粉状模型纳入到模拟到真实的深度学习管道中。我们在粉末模型上训练的网络获得的分类和本地化结果与在真实图像上训练的网络相当。此外，与使用原始的未粉化的CAD模型相比，人造粉末模型的优越性也得到了证明，特别是对于由于粉末积聚而与其CAD模型有显着差异的零件。