Image-based metrics for characterizing drilling induced defects in fibre-reinforced composite

Modern aircraft assembly involves drilling holes in multi-material stacks, followed by fastening to create a structural joint. Stringent quality requirements to ensure structural integrity necessitate accurate assessment of the hole quality. Carbon fibre reinforced polymer (CFRP) is widely used in aircraft structures, and delamination and uncut fibres are prominent defects associated with CFRP drilling. Furthermore, metal-CFRP composite structures, owing to their higher cost and hence the need reduced waste, present even more stringent requirements on hole quality. Defects such as delamination and uncut fibres in this composite also differ in how they manifest. An automated evaluation framework and robust hole quality metrics are therefore required to address this challenge. In this paper, an automated image processing based evaluation framework for rapid and objective assessment of delamination and uncut fibre defects is developed. This robust framework introduces combined metrics, leveraging a weighted p-norm approach to aggregate five factors for delamination and five for uncut fibres with p = 1, 2 and ∞. A method to choose weights for the different p-norms is also identified to create robust measures. Grayscale images captured via optical microscopy serve as inputs, and the framework outputs both the combined metrics and ten individual metrics for detailed analysis. The multi-objective space with the associated Pareto front and population based ranking derived from the weighted p-norm based metrics are introduced as a means to identify relative hole quality and to associate these with the machining parameters. A dataset comprising 24 holes from two metal-CFRP composite workpieces produced in a robotic drilling operation is used for demonstration of the framework. The results demonstrate the effectiveness of the proposed framework and its utility for real-world applications.