Abstract
The characterisation of fatigue damage evolution in constrained glass fibre reinforced plastic off-axis laminates is presented. A newly developed imaging technique known as Automatic Crack Counting (ACC) is used to quantify the off-axis crack state in constant amplitude (CA) and variable amplitude (VA) block loading tension-tension fatigue tests and constant amplitude compression-tension tests. The quantified crack states are analysed by combining the newly developed ACC method with a data mining approach and applying these to large data sets obtained during fatigue tests. It is shown that for a constant stress level, the stochastic nature of off-axis crack initiation and crack growth is accurately modelled by the Weibull distribution, with the distribution parameters being efficiently derived using the developed approach. The data-rich characterisation provides new insight in the crack density evolution process for VA and C-T loading, as well as derived Weibull distribution parameters in combination with the classical S-N curves and Paris’ Law relationship. Hence, providing an improved approach that includes the stochastic and deterministic information for physically based modelling of crack density evolution for fatigue loading.
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