A method to predict drilling-induced burr occurrence in chopped carbon fibre reinforced polymer (CFRP) composites based on digital image processing

Mechanical drilling-induced burr in carbon fibre reinforced polymer (CFRP) composites is one of the most sig-nificant 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.

Automated summary

The aim of this research work was to develop a method to predict drilling-induced burrs in chopped CFRPs based on digital image processing. By determining the fiber orientation of visible chopped fiber groups and predicting burr-dangerous regions based on the local properties of chopped fibers, the method enables the estimation of burrs without machining experiments in chopped CFRPs.