3D Representative Volume Element Reconstruction of Fiber Composites via Orientation Tensor and Substructure Features

Conference · Fri Jan 01 04:00:00 EST 2016

OSTI ID:1431010

Li, Yi ^[1]; Chen, Wei; Xu, Hongyi; Jin, Xuejun

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1. Ford Motor Company

To provide a seamless integration of manufacturing processing simulation and fiber microstructure modeling, two new stochastic 3D microstructure reconstruction methods are proposed for two types of random fiber composites: random short fiber composites, and Sheet Molding Compounds (SMC) chopped fiber composites. A Random Sequential Adsorption (RSA) algorithm is first developed to embed statistical orientation information into 3D RVE reconstruction of random short fiber composites. For the SMC composites, an optimized Voronoi diagram based approach is developed for capturing the substructure features of SMC chopped fiber composites. The proposed methods are distinguished from other reconstruction works by providing a way of integrating statistical information (fiber orientation tensor) obtained from material processing simulation, as well as capturing the multiscale substructures of the SMC composites.

• Research Organization: Ford Motor Company
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
• DOE Contract Number: EE0006867
• OSTI ID: 1431010
• Country of Publication: United States
• Language: English