Predicting the delamination factor in carbon fibre reinforced plastic composites during drilling through the Gaussian process regression

The carbon fibre reinforced plastic (CFPR) has been widely used in aircraft structural applications due to the superior modulus, specific tensile strength, and fatigue strength. The inhomogeneous and anisotropic nature of these composites poses great challenges on the machining process. Particularly, the delamination is one of major defects associated with drilling, which has a significant impact on CFRP's structure integrity and application. Machine learning approaches can help facilitate the optimization of machining processes. In this study, we develop the Gaussian process regression (GPR) model to predict delaminations in carbon fibre reinforced plastic composites during drilling from machining parameters. The model is simple and highly accurate and stable that contributes to fast delamination estimations. By combining the optimization results from the Taguchi method and GPR approach, it is expected that more quantitative data can be extracted from fewer experimental trials at the same time.

Automated summary

CFRP is widely used in aircraft structural applications due to its superior properties, but drilling-induced delamination is a major concern. In this study, a GPR model is developed to predict delaminations in CFRP composites during drilling, which, combined with the Taguchi method, can provide faster optimization of machining processes with more quantitative data extracted from fewer experimental trials.