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Title: A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling

Abstract

We report that carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain the material properties directly from experiments such as uniaxial tension and bias-extension where the coupling effect between tension and shear is not considered. Neglecting this coupling effect deteriorates the prediction accuracy of simulations. To address this issue, we develop a Bayesian model calibration and material characterization approach in a multiscale finite element preforming simulation framework that utilizes mesoscopic representative volume element (RVE) to account for the tension-shear coupling. A new geometric modeling technique is first proposed to generate the RVE corresponding to the close-packed uncured prepreg. This RVE model is then calibrated with a modular Bayesian approach to estimate the yarn properties, test its potential biases against the experiments, and fit a stress emulator. Finally, the predictive capability of this multiscale approach is further demonstrated by employing the stress emulator in the macroscale preforming simulation which shows that this approach can provide accurate predictions.

Authors:
 [1]; ORCiD logo [1];  [1];  [2];  [2]; ORCiD logo [3];  [4];  [1];  [1]
  1. Northwestern Univ., Evanston, IL (United States)
  2. Ford Motor Company, Dearborn, MI (United States)
  3. Delft University of Technology (Netherlands)
  4. Tongji University, Shanghai (China)
Publication Date:
Research Org.:
Ford Motor Company, Dearborn, MI (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1504735
Grant/Contract Number:  
EE0006867
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Composites Science and Technology
Additional Journal Information:
Journal Volume: 170; Journal Issue: C; Journal ID: ISSN 0266-3538
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; Prepreg; Preforming; Bayesian calibration; Gaussian processes; Multiscale simulations

Citation Formats

Zhang, Weizhao, Bostanabad, Ramin, Liang, Biao, Su, Xuming, Zeng, Danielle, Bessa, Miguel A., Wang, Yanchao, Chen, Wei, and Cao, Jian. A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling. United States: N. p., 2018. Web. doi:10.1016/j.compscitech.2018.11.019.
Zhang, Weizhao, Bostanabad, Ramin, Liang, Biao, Su, Xuming, Zeng, Danielle, Bessa, Miguel A., Wang, Yanchao, Chen, Wei, & Cao, Jian. A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling. United States. doi:10.1016/j.compscitech.2018.11.019.
Zhang, Weizhao, Bostanabad, Ramin, Liang, Biao, Su, Xuming, Zeng, Danielle, Bessa, Miguel A., Wang, Yanchao, Chen, Wei, and Cao, Jian. Mon . "A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling". United States. doi:10.1016/j.compscitech.2018.11.019.
@article{osti_1504735,
title = {A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling},
author = {Zhang, Weizhao and Bostanabad, Ramin and Liang, Biao and Su, Xuming and Zeng, Danielle and Bessa, Miguel A. and Wang, Yanchao and Chen, Wei and Cao, Jian},
abstractNote = {We report that carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain the material properties directly from experiments such as uniaxial tension and bias-extension where the coupling effect between tension and shear is not considered. Neglecting this coupling effect deteriorates the prediction accuracy of simulations. To address this issue, we develop a Bayesian model calibration and material characterization approach in a multiscale finite element preforming simulation framework that utilizes mesoscopic representative volume element (RVE) to account for the tension-shear coupling. A new geometric modeling technique is first proposed to generate the RVE corresponding to the close-packed uncured prepreg. This RVE model is then calibrated with a modular Bayesian approach to estimate the yarn properties, test its potential biases against the experiments, and fit a stress emulator. Finally, the predictive capability of this multiscale approach is further demonstrated by employing the stress emulator in the macroscale preforming simulation which shows that this approach can provide accurate predictions.},
doi = {10.1016/j.compscitech.2018.11.019},
journal = {Composites Science and Technology},
issn = {0266-3538},
number = C,
volume = 170,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
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