Mechanical and thermodynamical properties and thus machinability of carbon fibre reinforced
polymer composites significantly depend on the fibre orientation relative to the load
direction. However, the orientations of the fibre groups in polymer composites reinforced
by chopped carbon fibres are stochastic; therefore, the properties and machinability
of such composites are challenging to plan, predict and optimise. We developed four
different and novel approaches for fibre detection in polymer composites reinforced
by chopped carbon fibres: (i) detecting the fibres through naked eye supported manual
drawing, (ii) digital image processing of optical images, (iii) machine learning-based
fibre detection, and (iv) rectangle fitting on the outputs of the automated processes
using the Chaudhuri and Samal method. The applicability of the novel approaches was
tested through optically captured images of polymer composites reinforced by chopped
carbon fibres. The developed methods are each capable of detecting fibre groups at
the top and bottom of the composite plate with certain limitations. The rectangle
fitting approaches performed the best from the point of view of correctly identifying
of fibre groups, followed by the machine learning-based and the conventional digital
image processed, respectively. As a result of this study, the machining process planning
and condition monitoring of polymer composites reinforced by chopped carbon fibres
is more deeply supported.
