Prediction of carbon nanotubes reinforced interphase properties in fuzzy fibre reinforced polymer via inverse analysis and optimisation
Journal article
Pawlik, M., Le, H., Wood, P. and Lu, Y. 2023. Prediction of carbon nanotubes reinforced interphase properties in fuzzy fibre reinforced polymer via inverse analysis and optimisation. Computational Material Science. 231, pp. 1-11. https://doi.org/10.1016/j.commatsci.2023.112548
| Authors | Pawlik, M., Le, H., Wood, P. and Lu, Y. |
|---|---|
| Abstract | This paper presents the parameter identification procedure of carbon nanotubes (CNTs) reinforced interphase in fuzzy fibre reinforced polymer (FFRP). The procedure was completed with ANSYS Workbench 19.2 software by combining Mechanical ADPL and Goal-Driven Optimisation. Firstly, a three-phase representative volume element (RVE) containing carbon fibre, CNTs reinforced interphase and epoxy resin was developed as a collection of Mechanical APDL commands. This RVE model was simulated to evaluate the elastic constants of FFRP lamina. CNTs reinforced interphase was characterised by transversely isotropic model. Interphase properties were parametrised and became input parameters in the Goal-Driven Optimisation. FFRP lamina elastic constants were set as the output parameters. Multi-objective Genetic Algorithm (MOGA) was used to identify the interphase properties, so that the output FFRP lamina elastic constants match the objective and constraints. The optimisation algorithm converged after 585 evaluations. Five potential candidate point, which met required objectives and constraints, were found. The identified interphase properties agreed well with the literature (an average percentage error of around 2%). This inverse procedure shows the potential to identify the interphase properties in nano-engineered composites, which are extremely difficult to measure experimentally |
| Keywords | Fuzzy fibre reinforced polymer; Nano-reinforced interphase; Inverse problem; Parameters’ identification; Optimisation algorithm |
| Year | 2023 |
| Journal | Computational Material Science |
| Journal citation | 231, pp. 1-11 |
| Publisher | Elsevier |
| ISSN | 1879-0801 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.commatsci.2023.112548 |
| Web address (URL) | https://www.sciencedirect.com/science/article/pii/S0927025623005426 |
| Funder | University of Derby |
| Accepted author manuscript | File Access Level Open |
| Publisher's version | License File Access Level Open |
| Output status | Published |
| Publication dates | |
| Online | 12 Oct 2023 |
| Publication process dates | |
| Accepted | 01 Oct 2023 |
| Deposited | 20 Oct 2023 |
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