A coupled thermo‐chemo‐mechanical reduced‐order multiscale model for predicting process‐induced distortions, residual stresses, and strength
Abstract
Summary We study residual stresses and part distortion induced by a manufacturing process of a polymer matrix composite and its effect on the component strength. Unlike most of the thermo‐chemo‐mechanical models in the literature where governing multiphysics equations are directly formulated on the macroscale, we present a multiscale‐multiphysics approach. To address the enormous computational complexity involved, a reduced‐order homogenization was originally developed for a single physics problem is employed. The proposed reduced‐order two‐scale thermo‐chemo‐mechanical model has been validated for predicting part distortion beam strength in three‐point bending test. It is shown that while macroscopic stresses are relatively low, and therefore often ignored in practice, stresses at the scale of microconstituents are significant and may have an effect on the overall composite component strength.
- Authors:
-
[1];
[3]
- Center for Applied Physics and Technology Peking University Beijing China, Department of Mechanics and Engineering Science Peking University Beijing China, Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing China
- General Motors R&,D Technical Center General Motors Company Warren Michigan
- Department of Civil Engineering and Engineering Mechanics Columbia University New York New York
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1576856
- Grant/Contract Number:
- DE‐EE0006826
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- International Journal for Numerical Methods in Engineering
- Additional Journal Information:
- Journal Name: International Journal for Numerical Methods in Engineering Journal Volume: 121 Journal Issue: 7; Journal ID: ISSN 0029-5981
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Yuan, Zifeng, Aitharaju, Venkat, and Fish, Jacob. A coupled thermo‐chemo‐mechanical reduced‐order multiscale model for predicting process‐induced distortions, residual stresses, and strength. United Kingdom: N. p., 2019.
Web. doi:10.1002/nme.6274.
Yuan, Zifeng, Aitharaju, Venkat, & Fish, Jacob. A coupled thermo‐chemo‐mechanical reduced‐order multiscale model for predicting process‐induced distortions, residual stresses, and strength. United Kingdom. https://doi.org/10.1002/nme.6274
Yuan, Zifeng, Aitharaju, Venkat, and Fish, Jacob. Tue .
"A coupled thermo‐chemo‐mechanical reduced‐order multiscale model for predicting process‐induced distortions, residual stresses, and strength". United Kingdom. https://doi.org/10.1002/nme.6274.
@article{osti_1576856,
title = {A coupled thermo‐chemo‐mechanical reduced‐order multiscale model for predicting process‐induced distortions, residual stresses, and strength},
author = {Yuan, Zifeng and Aitharaju, Venkat and Fish, Jacob},
abstractNote = {Summary We study residual stresses and part distortion induced by a manufacturing process of a polymer matrix composite and its effect on the component strength. Unlike most of the thermo‐chemo‐mechanical models in the literature where governing multiphysics equations are directly formulated on the macroscale, we present a multiscale‐multiphysics approach. To address the enormous computational complexity involved, a reduced‐order homogenization was originally developed for a single physics problem is employed. The proposed reduced‐order two‐scale thermo‐chemo‐mechanical model has been validated for predicting part distortion beam strength in three‐point bending test. It is shown that while macroscopic stresses are relatively low, and therefore often ignored in practice, stresses at the scale of microconstituents are significant and may have an effect on the overall composite component strength.},
doi = {10.1002/nme.6274},
journal = {International Journal for Numerical Methods in Engineering},
number = 7,
volume = 121,
place = {United Kingdom},
year = {Tue Dec 03 00:00:00 EST 2019},
month = {Tue Dec 03 00:00:00 EST 2019}
}
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