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

Abstract

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.

Authors:
 [1];  [2];  [3];  [4];  [4];  [4];  [4];  [2];  [4]
+ Show Author Affiliations
  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
Publication Date:
Research Org.:
Ford Motor Company, Detroit, MI (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); China Scholarship Council (CSC); USDOE
OSTI Identifier:
1431177
Alternate Identifier(s):
OSTI ID: 1548980
Grant/Contract Number:  
EE0006867
Resource Type:
Accepted Manuscript
Journal Name:
Composite Structures
Additional Journal Information:
Journal Volume: 188; Journal Issue: C; Journal ID: ISSN 0263-8223
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 42 ENGINEERING; 97 MATHEMATICS AND COMPUTING; SMC; Multiscale; RVE; Mesostructure reconstruction; Orientation tensor

Citation Formats

Chen, Zhangxing, Huang, Tianyu, Shao, Yimin, Li, Yang, Xu, Hongyi, Avery, Katherine, Zeng, Danielle, Chen, Wei, and Su, Xuming. Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction. United States: N. p., 2018. Web. doi:10.1016/j.compstruct.2017.12.039.
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Chen, Zhangxing, Huang, Tianyu, Shao, Yimin, Li, Yang, Xu, Hongyi, Avery, Katherine, Zeng, Danielle, Chen, Wei, & Su, Xuming. Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction. United States. https://doi.org/10.1016/j.compstruct.2017.12.039
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Chen, Zhangxing, Huang, Tianyu, Shao, Yimin, Li, Yang, Xu, Hongyi, Avery, Katherine, Zeng, Danielle, Chen, Wei, and Su, Xuming. Thu . "Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction". United States. https://doi.org/10.1016/j.compstruct.2017.12.039. https://www.osti.gov/servlets/purl/1431177.
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@article{osti_1431177,
title = {Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction},
author = {Chen, Zhangxing and Huang, Tianyu and Shao, Yimin and Li, Yang and Xu, Hongyi and Avery, Katherine and Zeng, Danielle and Chen, Wei and Su, Xuming},
abstractNote = {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.},
doi = {10.1016/j.compstruct.2017.12.039},
journal = {Composite Structures},
number = C,
volume = 188,
place = {United States},
year = {Thu Mar 15 00:00:00 EDT 2018},
month = {Thu Mar 15 00:00:00 EDT 2018}
}
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https://doi.org/10.1016/j.compstruct.2017.12.039
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    • DOI: 10.3390/ma12030538

    Transfer learning of deep material network for seamless structure–property predictions
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