Gépészmérnöki tudományok és gyártástervezés (alakítás, szerelés, csatlakozás, szétválasztás)
Mechanical drilling-induced burr in carbon fibre reinforced polymer (CFRP) composites
is one of the most significant macro-geometrical failures of CFRP composites; nevertheless,
burr prediction in quasi-randomly oriented chopped fibre reinforced composites is
not supported yet. To explore this issue, the main aim of the present research work
was to develop a method to predict drilling-induced burrs in chopped CFRPs based on
digital image processing. First, an indexable light source captured digital images
of a chopped CFRP plate in different lighting conditions. Then, the fibre orientation
of each visible chopped fibre group was determined in each image through digital image
processing algorithms. These images were then associated based on the superposition
principle. Finally, the burr-dangerous regions were predicted by the local properties
of chopped fibres. The prediction accuracy of the algorithm is tested by drilling
experiments in chopped CFRP plates using solid carbide drills. The experimental results
show that the mechanical drilling-induced burr prediction accuracy is 64–97%. By applying
the proposed method, burrs can be estimated without machining experiments in chopped
CFRPs.