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

Abstract

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.

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [5];  [3]
  1. Northwestern Univ., Evanston, IL (United States); Shanghai Jiao Tong University (China)
  2. Chongqing University (China); Ford Motor Company, Dearborn, MI (United States)
  3. Ford Motor Company, Dearborn, MI (United States)
  4. Northwestern Univ., Evanston, IL (United States)
  5. Shanghai Jiao Tong University (China)
Publication Date:
Research Org.:
Ford Motor Company, Dearborn, MI (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1504736
Alternate Identifier(s):
OSTI ID: 1582982
Grant/Contract Number:  
EE0006867
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Composite Structures
Additional Journal Information:
Journal Volume: 200; Journal Issue: C; Journal ID: ISSN 0263-8223
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 36 MATERIALS SCIENCE; SMC composites; Microstructure reconstruction; Orientation tensor; Fiber tow; RVE

Citation Formats

Li, Yi, Chen, Zhangxing, Su, Lingxuan, Chen, Wei, Jin, Xuejun, and Xu, Hongyi. Stochastic reconstruction and microstructure modeling of SMC chopped fiber composites. United States: N. p., 2018. Web. doi:10.1016/j.compstruct.2018.05.079.
Li, Yi, Chen, Zhangxing, Su, Lingxuan, Chen, Wei, Jin, Xuejun, & Xu, Hongyi. Stochastic reconstruction and microstructure modeling of SMC chopped fiber composites. United States. doi:10.1016/j.compstruct.2018.05.079.
Li, Yi, Chen, Zhangxing, Su, Lingxuan, Chen, Wei, Jin, Xuejun, and Xu, Hongyi. Sat . "Stochastic reconstruction and microstructure modeling of SMC chopped fiber composites". United States. doi:10.1016/j.compstruct.2018.05.079. https://www.osti.gov/servlets/purl/1504736.
@article{osti_1504736,
title = {Stochastic reconstruction and microstructure modeling of SMC chopped fiber composites},
author = {Li, Yi and Chen, Zhangxing and Su, Lingxuan and Chen, Wei and Jin, Xuejun and Xu, Hongyi},
abstractNote = {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.},
doi = {10.1016/j.compstruct.2018.05.079},
journal = {Composite Structures},
issn = {0263-8223},
number = C,
volume = 200,
place = {United States},
year = {2018},
month = {5}
}

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