Region-based online flaw detection of 3D printing via fringe projection

Abstract

Additive manufacturing allows for the production of custom parts with complex internal features. However, a lack of online detection of the printing process is one of the challenges that has prevented further improvements in additive manufacturing. This paper proposes a novel online monitoring technique based on a fringe projector to keep the stability of the process under control on a layer-by-layer basis. To detect microdefects with a magnitude smaller than the noise, we propose a region-based defect detection method that takes each subregion, instead of each point, as a detection unit. Compared with a single point, a sub region contains hundreds of points, which represent a domain with greater stability. We combine voxel cloud connectivity segmentation (VCCS) and fast point feature histogram (FPFH) to divide the printing area into hundreds of subregions, smooth the surface to resist the noise based on moving least square (MLS) and evaluate each subregion using local point features. The projection model and normal deviation are developed to extract the sizes and locations of the flaws. The performances of the techniques are demonstrated with some experiments using different 3D printing objects, which indicate that the proposed method is able to detect various flaws and its detection accuracy is higher than the noise magnitude.