Automated Deviation-Aware Landmark Selection for Freeform Product Accuracy Qualification in 3D Printing

AbstractLandmarks are essential in non-rigid shape registration for identifying the correspondence between designs and actual products. In 3D printing, manual selection of landmarks becomes labor-intensive due to complex product geometries and their non-uniform shape deviations. Automatic selection, however, has to pinpoint landmarks indicative of geometric regions prone to deviations for accuracy qualification. Existing automatic landmarking methods often generate clustered and redundant landmarks for prominent features with high curvatures, compromising the balance between global and local registration errors. To address these issues, we propose an automatic landmark selection method through deviation-aware segmentation and landmarking. As opposed to segmentation for semantic feature identification, deviation-aware segmentation partitions a freeform product for high-curvature region identification. Prone to deviation, these regions are generated through curvature-sensitive remeshing to extract vertices of high curvature and automatic clustering of vertices based on vertex density. Within each segment or high-curvature region, a curvature-weighted function is tailored for the Gaussian process landmarking to sequentially select landmarks with the highest local curvatures. Furthermore, we propose a new evaluation criterion to assess the effectiveness of selected landmarks through registration. The proposed approach is tested through automatic landmarking of printed dental models.Keywords: 3D printing qualificationnon-rigid shape registrationshape segmentationclusteringGaussian process landmarkingDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsWeizhi LinWeizhi Lin is a PhD student in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California (USC) in Los Angeles. She completed her B.E. degree in Statistics at Beihang University in 2019. Her research focuses on leveraging domain knowledge to develop models for analyzing complex manifold data, with a specific emphasis on addressing challenges in the field of advanced manufacturing.Qiang HuangDr. Qiang Huang is a professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research focuses on Machine Learning for Smart Manufacturing and Quality Control for Personalized Manufacturing. He was the holder of the Gordon S. Marshall Early Career Chair in Engineering at USC from 2012 to 2016. He received the IISE Fellow Award, ASME Fellow Award, NSF CAREER Award, 2021 IEEE CASE Best Conference Paper Award, and 2013 IEEE Transactions on Automation Science and Engineering Best Paper Award, among others. He holds five patents on quality control for additive manufacturing. He was a Department Editor for IISE Transactions and an Associate Editor for ASME Transactions, Journal of Manufacturing Science and Engineering.