Knowledge of the permeability tensor in liquid composite molding is important for process optimization. Unfortunately, experimental determination of permeability is difficult and time consuming. A rapid, non-destructive technique called optical coherence tomography (OCT) can image the microstructure of a composite in minutes. In this work, binary images were generated from the low contrast OCT data through image de-noising, contrast enhancement and feature recognition. The images were then subjected to an additional pattern recognition routine that smoothed the images without sacrificing fiber volume fraction. The resulting data were input to a lattice Boltzmann model for permeability prediction. The influence of the fiber volume fraction, tow surface area, average mean free channel path, and variable microstructure are discussed in terms of their individual and synergistic effects on permeability. The calculated axial and transverse permeabilities from the post-processed images show excellent agreement with the experimental values.
Citation: Polymer Composites
Issue: No. 6
Pub Type: Journals
composites, imaging, optical coherence tomography, permeability