Additive manufacturing (AM) is a new technology for fabricating products straight from a 3D digital model, which can lower costs, minimize waste, and increase building speed while maintaining acceptable quality. However, it still suffers from low dimensional accuracy and a lack of geometrical quality standards. Moreover, there is a need for a robust AM configuration to perform in-situ inspections during the fabrication. This work established a 3D printing-scanning setup to collect 3D point cloud data of printed parts and then compare them with nominal 3D point cloud data to quantify the deviation in all X, Y, and Z directions. Specifically, this work aims at predicting the anticipated deviation along the Z direction by applying a deep learning-based prediction model. An experiment with regard to a human “Knee” prototype fabricated by Fused Deposition Modeling (FDM) is conducted to show the effectiveness of the proposed methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.