An inverse modeling approach for predicting filled rubber performance

Jiaying Gao, Modesar Shakoor, Hiroshi Jinnai, Hiroshi Kadowaki, Eisuke Seta, Wing Kam Liu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number112567
JournalComputer Methods in Applied Mechanics and Engineering
Volume357
DOIs
Publication statusPublished - 2019 Dec 1

Keywords

  • Database for self-consistent clustering analysis (SCA)
  • Interphase
  • Nano-composites
  • Two stage offline–online reduced order modeling

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications

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