A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling. (20th January 2019)

• Record Type: Journal Article
• Authors: Zhang, Weizhao; Bostanabad, Ramin; Liang, Biao; Su, Xuming; Zeng, Danielle; Bessa, Miguel A.; Wang, Yanchao; Chen, Wei; Cao, Jian
• Is Part Of: Composites science and technology. Volume 170(2019)
• Page Start: 15
• Page End: 24
• Publication Date: 2019-01-20
• Subjects: Prepreg -- Preforming -- Bayesian calibration -- Gaussian processes -- Multiscale simulations; Composite materials -- Periodicals; Composite materials; Fibrous composites; Periodicals; 620.118
• DOI: 10.1016/j.compscitech.2018.11.019
• Languages: English
• ISSNs: 0266-3538

Abstract

Abstract: 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.