Stochastic reconstruction and microstructure modeling of SMC chopped fiber composites

Li, Yi; Chen, Zhangxing; Su, Lingxuan; Chen, Wei; Jin, Xuejun; Xu, Hongyi

doi:10.1016/j.compstruct.2018.05.079

Title: Stochastic reconstruction and microstructure modeling of SMC chopped fiber composites

Journal Article · Sat May 19 00:00:00 EDT 2018 · Composite Structures

DOI:https://doi.org/10.1016/j.compstruct.2018.05.079· OSTI ID:1504736

^[1]; Chen, Zhangxing ^[2]; Su, Lingxuan ^[3]; Chen, Wei ^[4]; Jin, Xuejun ^[5]; Xu, Hongyi ^[3]

Northwestern Univ., Evanston, IL (United States); Shanghai Jiao Tong University (China)
Chongqing University (China); Ford Motor Company, Dearborn, MI (United States)
Ford Motor Company, Dearborn, MI (United States)
Northwestern Univ., Evanston, IL (United States)
Shanghai Jiao Tong University (China)

Here, to establish an integrated Processing-Microstructure-Property workflow for the prediction of material behaviors, this paper presents a new stochastic pseudo-3D microstructure reconstruction method for Sheet Molding Compounds (SMC) chopped fiber composites. The proposed method captures the bi-level microstructural features of SMC composites. At the higher level, a Voronoi diagram-based algorithm is developed to reconstruct the unique substructure features of SMC fiber tows. The geometry of Voronoi cells is adjusted by a Simulated Annealing (SA) algorithm to match the geometrical statistics of the real fiber tows. At the lower level, the algorithm assigns fiber orientation to each Voronoi cell (which represents a fiber tow). The fiber orientation angles are recovered from a statistical fiber orientation tensor. The proposed method is employed to establish a multi-layer pseudo-3D SMC Representative Volume Element (RVE) model for Finite Element Analysis (FEA) of SMC microstructure. This model enables the prediction of mechanical properties based on the material processing information (e.g. fiber orientation tensor obtained from compression molding simulation), and the microstructure information obtained from microscopic imaging for an SMC composite. Lastly, the predicted properties are successfully validated by experimental tensile tests.

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Research Organization:: Ford Motor Company, Detroit, MI (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); USDOE

Grant/Contract Number:: EE0006867

OSTI ID:: 1504736

Alternate ID(s):: OSTI ID: 1582982

Journal Information:: Composite Structures, Vol. 200, Issue C; ISSN 0263-8223

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 18 works

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