Abstract
This paper discusses two alternative modelling approaches for describing fatigue delamination growth (FDG) in polymer-based fibre-reinforced composites, i.e. semi-empirical equations having a power-law form and artificial neural networks. Barenblatt's self-similarity principles are applied for identifying a suitable expression of the delamination driving force in terms of the square-rooted energy-release-rate range and the associated peak values. The general dependency of pre-factors and exponents of FDG power-laws on the stress-ratio and mode-mixity is discussed in detail. Single-hidden-layer neural networks (SHLNN) with the support of self-similarity principles are here proposed as an alternative to semi-empirical power laws for describing FDG in composites. A example application of SHLNN to mixed-mode and variable stress-ratio FDG is provided for the carbon/epoxy system T800H/#3631. The SHLNN predictions are compared to a semi-empirical fit based on a modified Hartman-Schijve power-law.
Original language | English |
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Pages (from-to) | 59-70 |
Number of pages | 12 |
Journal | Materials and Design |
Volume | 155 |
Early online date | 26 May 2018 |
DOIs | |
Publication status | Published - 5 Oct 2018 |
Keywords
- Artificial neural networks
- Composites
- Delamination
- Fatigue
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Professor Giuliano Allegri
- School of Civil, Aerospace and Design Engineering - Professor of Structural Integrity of Composites
- Composites University Technology Centre (UTC)
- Bristol Composites Institute
Person: Academic , Member