SMMG Seminar: Statistical Shape Modeling for Custom Design and Analysis
ABSTRACT
Mining and analysis of massive population-based shape data can result in knowledge of shape variability of the population. Such knowledge can lead to the construction of faithful subject-specific 3D shape models from sparse measurements, predict shape-specific functional performance and population-wide structural performance variation. Such an ability brings about unprecedented capabilities and tantalizing opportunities for mass customization, part-specific failure prediction and just-in-time part maintenance, and patient-specific biomedical intervention and treatment. This research proposes a statistical atlas based approach that incorporates statistical shape modeling in subject-specific shape reconstruction, finite element (FE) modeling and analysis. The statistical atlas contains three parts: the mean shape and the variation modes of the shape population which span a linear shape space, the FE mesh of the mean shape (template mesh), and the selected feature points and sizing dimensions which are obtained by maximizing the total variance they capture of the shape population. Given a subject (e.g. a person), the corresponding dimensions are measured and the 3D shape model is synthesized. The template mesh can be morphed to the subject shape to conduct subject-specific FE analysis. The FE solution on the template mesh can also be extrapolated to the subject shape through Taylor expansion. The shape variances along the variation modes are obtained by the principal component analysis. These variances tell the amount of shape variabilities along the variation modes and are combined with the Taylor expansion of the FE solution to obtain the structural performance variation across the population. The 2D/3D numerical examples demonstrate the efficiency and effectiveness of the proposed approach.
BIOGRAPHY
Xilu Wang got his Bachelor’s degree in 2010 from Huazhong University of Science and Technology, where he majored in mechanical engineering. In 2011, he started his PhD in Illinois Institute of Technology (IIT) at Chicago with his research focused on volumetric parametrization and isogeometric analysis. In January 2014, he transferred to UW Madison with his lab. In UW Madison, he focused on data-drive design and analysis and developed methods to learn from existing shape data and applied the learned statistical shape model in geometric design and finite element analysis. In the fall of 2017, he had his internship in Siemens Corporate Technology, where he worked on GPU based high performance computing for multi-scale modeling. He graduated from UW Madison in December 2017 and joined the MSC Software Corporation. In MSC Software he is a development engineer with expertise in computational geometry, finite element analysis and meshing. He jointed Gamma Technologies (GT) in April 2019 and he is now a senior software development engineer in GT with expertise in solid modeling and computational geometry.