TY - JOUR
T1 - A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling
AU - Zhang, Weizhao
AU - Bostanabad, Ramin
AU - Liang, Biao
AU - Su, Xuming
AU - Zeng, Danielle
AU - Bessa, Miguel A.
AU - Wang, Yanchao
AU - Chen, Wei
AU - Cao, Jian
N1 - Accepted Author Manuscript
PY - 2019
Y1 - 2019
N2 - 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. 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.
AB - 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. 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.
KW - Bayesian calibration
KW - Gaussian processes
KW - Multiscale simulations
KW - Preforming
KW - Prepreg
UR - http://www.scopus.com/inward/record.url?scp=85056872580&partnerID=8YFLogxK
U2 - 10.1016/j.compscitech.2018.11.019
DO - 10.1016/j.compscitech.2018.11.019
M3 - Article
AN - SCOPUS:85056872580
SN - 0266-3538
VL - 170
SP - 15
EP - 24
JO - Composites Science and Technology
JF - Composites Science and Technology
ER -