Void content computation using optical microscopy for carbon fiber composites

Abstract: Three different void content calculation techniques using optical microscopy werecompared in multiple-user trials. The three methods studied comprised of a selection,thresholding, and semi-automatic machine learning method. The techniques wereapplied to micrographs of three carbon fiber-epoxy composite plates manufacturedin-house, where one plate had reduced void content by means of debulking priorto curing. The users performed the techniques on the sets of micrographs and thestandard deviation between the users void content results were measured.The advantages of the three methods were discussed and their practical applications wereproposed. The trials showed agreement between users on what are voids and not as well asshowing that uncertainties in void content are specimen-specific and not attributed todifferent users or methods applied. All three methods showed satisfying precision incalculating void content compared to void content quality levels provided by literature.It was found that thresholding, which is the current standard method of void contentcalculation using microscopy, inhabits an unscientific bias which compromises the legitimacyof the method. The study formulates a manual selection-based method usingedge-detection selection tools intended to benchmark void content in images, as wellas proposing a route to the automation of void content analysis using microscopy.