TY - JOUR
T1 - An inverse modeling approach for predicting filled rubber performance
AU - Gao, Jiaying
AU - Shakoor, Modesar
AU - Jinnai, Hiroshi
AU - Kadowaki, Hiroshi
AU - Seta, Eisuke
AU - Liu, Wing Kam
N1 - Funding Information:
Jiaying Gao, Modesar Shakoor, and Wing Kam Liu gratefully acknowledge the partial support of the Bridgestone Corporation. Wing Kam Liu acknowledged the partial supported by National Science Foundation grant CMMI-1762035. The authors also greatly acknowledge the contribution of Dr. Takeshi Higuchi and Mr. Masatoshi Hirata in the experimental part of this study. This study was partially supported by Japan Society for the Promotion of Science KAKENHI, Japan (Grant No. 16H02288).
Funding Information:
Jiaying Gao, Modesar Shakoor, and Wing Kam Liu gratefully acknowledge the partial support of the Bridgestone Corporation. Wing Kam Liu acknowledged the partial supported by National Science Foundation grant CMMI-1762035 . The authors also greatly acknowledge the contribution of Dr. Takeshi Higuchi and Mr. Masatoshi Hirata in the experimental part of this study. This study was partially supported by Japan Society for the Promotion of Science KAKENHI, Japan (Grant No. 16H02288 ).
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - In this paper, a computational procedure combining experimental data and interphase inverse modeling is presented to predict filled rubber compound properties. The Fast Fourier Transformation (FFT) based numerical homogenization scheme is applied on the high quality filled rubber 3D Transmission Electron Microscope (TEM) image to compute its complex shear moduli. The 3D TEM filled rubber image is then compressed into a material microstructure database using a novel Reduced Order Modeling (ROM) technique, namely Self-consistent Clustering Analysis (a two-stage offline database creation from training and learning, followed by data compression via unsupervised learning, and online prediction approach), for improved efficiency and accuracy. An inverse modeling approach is formulated for quantitatively computing interphase complex shear moduli in order to understand the interphase behaviors. The two-stage SCA and the inverse modeling approach formulate a three-step prediction scheme for studying filled rubber, whose loss tangent curve can be computed in agreement with test data.
AB - In this paper, a computational procedure combining experimental data and interphase inverse modeling is presented to predict filled rubber compound properties. The Fast Fourier Transformation (FFT) based numerical homogenization scheme is applied on the high quality filled rubber 3D Transmission Electron Microscope (TEM) image to compute its complex shear moduli. The 3D TEM filled rubber image is then compressed into a material microstructure database using a novel Reduced Order Modeling (ROM) technique, namely Self-consistent Clustering Analysis (a two-stage offline database creation from training and learning, followed by data compression via unsupervised learning, and online prediction approach), for improved efficiency and accuracy. An inverse modeling approach is formulated for quantitatively computing interphase complex shear moduli in order to understand the interphase behaviors. The two-stage SCA and the inverse modeling approach formulate a three-step prediction scheme for studying filled rubber, whose loss tangent curve can be computed in agreement with test data.
KW - Database for self-consistent clustering analysis (SCA)
KW - Interphase
KW - Nano-composites
KW - Two stage offline–online reduced order modeling
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U2 - 10.1016/j.cma.2019.112567
DO - 10.1016/j.cma.2019.112567
M3 - Article
AN - SCOPUS:85070308229
SN - 0045-7825
VL - 357
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
M1 - 112567
ER -