Stochastic modeling of a glass fiber reinforced polymer
Résumé
Stochastic modeling of a material microstructure is in general composed of multiple steps. First, geometric properties of the sample are measured by image analysis. Second, an appropriate stochastic model is chosen and model parameters are estimated from the geometric properties. Third, additional characteristics are computed on the data set and on realizations of stochastic models to evaluate the quality of the fitting. In this article, we show how to measure geometric properties of a fiber system, estimate parameters for two different fiber models, and evaluate the realizations with orientation covariance and tortuosity of the fibers. The considered stochastic models are the newly developed bended fiber model, composed of a force-biased packing of ball chains, and the classical cherry-pit cylinder model. We show the advantages and limitations of both methods.
Mots clés
Cylinder model
Data sets
Fiber model
Fiber reinforced composites
Fiber Separation
Fiber systems
Geometric properties
Glass fiber reinforced polymer
Material microstructures
Model parameters
Stochastic modeling
Fiber reinforced plastics
Geometry
Mathematical morphology
Parameter estimation
Quality control
Signal processing
Stochastic systems
439-450Stochastic models