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

Journal Article · · Composite Structures
 [1];  [2];  [3];  [4];  [4];  [4];  [4];  [2];  [4]
  1. Chongqing Univ. (China). State Key Lab. of Mechanical Transmission; Ford Motor Company, Dearborn, MI (United States). Research and Advanced Engineering
  2. Northwestern Univ., Evanston, IL (United States)
  3. Chongqing Univ. (China). State Key Lab. of Mechanical Transmission
  4. Ford Motor Company, Dearborn, MI (United States). Research and Advanced Engineering
+ Show Author Affiliations

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.

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); China Scholarship Council (CSC); USDOE
Grant/Contract Number:
EE0006867
OSTI ID:
1431177
Alternate ID(s):
OSTI ID: 1548980
Journal Information:
Composite Structures, Vol. 188, Issue C; ISSN 0263-8223
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 34 works
Citation information provided by
Web of Science

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  • Kovesi, Peter
  • 2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA) https://doi.org/10.1109/DICTA.2012.6411698
conference December 2012

Cited By (2)

Stochastic Constitutive Model of Isotropic Thin Fiber Networks Based on Stochastic Volume Elements journal February 2019
Transfer learning of deep material network for seamless structure–property predictions journal April 2019

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Related Subjects

36 MATERIALS SCIENCE
42 ENGINEERING
97 MATHEMATICS AND COMPUTING
SMC
Multiscale
RVE
Mesostructure reconstruction
Orientation tensor