Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction

Chen, Zhangxing; Huang, Tianyu; Shao, Yimin; Li, Yang; Xu, Hongyi; Avery, Katherine; Zeng, Danielle; Chen, Wei; Su, Xuming

doi:10.1016/j.compstruct.2017.12.039

Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction

Journal Article · Thu Mar 15 00:00:00 EDT 2018 · Composite Structures

DOI:https://doi.org/10.1016/j.compstruct.2017.12.039· OSTI ID:1431177

Chen, Zhangxing ^[1]; Huang, Tianyu ^[2]; Shao, Yimin ^[3]; Li, Yang ^[4]; Xu, Hongyi ^[4]; Avery, Katherine ^[4]; Zeng, Danielle ^[4]; Chen, Wei ^[2]; Su, Xuming ^[4]

Chongqing Univ. (China). State Key Lab. of Mechanical Transmission; Ford Motor Company, Dearborn, MI (United States). Research and Advanced Engineering; Ford Motor Company
Northwestern Univ., Evanston, IL (United States)
Chongqing Univ. (China). State Key Lab. of Mechanical Transmission
Ford Motor Company, Dearborn, MI (United States). Research and Advanced Engineering

Predicting the mechanical behavior of the chopped carbon fiber Sheet Molding Compound (SMC) due to spatial variations in local material properties is critical for the structural performance analysis but is computationally challenging. Such spatial variations are induced by the material flow in the compression molding process. In this work, a new multiscale SMC modeling framework and the associated computational techniques are developed to provide accurate and efficient predictions of SMC mechanical performance. The proposed multiscale modeling framework contains three modules. First, a stochastic algorithm for 3D chip-packing reconstruction is developed to efficiently generate the SMC mesoscale Representative Volume Element (RVE) model for Finite Element Analysis (FEA). A new fiber orientation tensor recovery function is embedded in the reconstruction algorithm to match reconstructions with the target characteristics of fiber orientation distribution. Second, a metamodeling module is established to improve the computational efficiency by creating the surrogates of mesoscale analyses. Third, the macroscale behaviors are predicted by an efficient multiscale model, in which the spatially varying material properties are obtained based on the local fiber orientation tensors. Our approach is further validated through experiments at both meso- and macro-scales, such as tensile tests assisted by Digital Image Correlation (DIC) and mesostructure imaging.

View Accepted Manuscript (DOE)

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

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

Grant/Contract Number:: EE0006867

OSTI ID:: 1431177

Alternate ID(s):: OSTI ID: 1548980

Journal Information:: Composite Structures, Journal Name: Composite Structures Journal Issue: C Vol. 188; ISSN 0263-8223

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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