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An ABAQUS® plug-in for generating virtual data required for inverse analysis of unidirectional composites using artificial neural networks

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E-pub ahead of print
  • Y. Ismail
  • L. Wan
  • J. Chen
  • J. Ye
  • D. Yang
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<mark>Journal publication date</mark>31/10/2021
<mark>Journal</mark>Engineering with Computers
Number of pages13
Publication StatusE-pub ahead of print
Early online date31/10/21
<mark>Original language</mark>English

Abstract

This paper presents a robust ABAQUS® plug-in called Virtual Data Generator (VDGen) for generating virtual data for identifying the uncertain material properties in unidirectional lamina through artificial neural networks (ANNs). The plug-in supports the 3D finite element models of unit cells with square and hexagonal fibre arrays, uses Latin-Hypercube sampling methods and robustly imposes periodic boundary conditions. Using the data generated from the plug-in, ANN is demonstrated to explicitly and accurately parameterise the relationship between fibre mechanical properties and fibre/matrix interphase parameters at microscale and the mechanical properties of a UD lamina at macroscale. The plug-in tool is applicable to general unidirectional lamina and enables easy establishment of high-fidelity micromechanical finite element models with identified material properties.