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

Ismail, Y. and Wan, L. and Chen, J. and Ye, J. and Yang, D. (2022) An ABAQUS® plug-in for generating virtual data required for inverse analysis of unidirectional composites using artificial neural networks. Engineering with Computers, 38 (5): 5. pp. 4323-4335. ISSN 0177-0667

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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.