We show that it is possible to predict the adhesion of metal layers on laser-machined fiber-reinforced plastic (FRP) components using hyperspectral imaging (HSI) and AI data analysis. 

For this purpose, FRP flat substrates were processed by a laser in order to increase the adhesion of the metal layer to be applied. The machined samples were characterized by HSI. Afterwards, a copper layer was applied by thermal spraying. Finally, a model to predict the adhesion of the copper layer based on the HSI data was trained and validated.

The developed method could be used for process monitoring in the production of hybrid components made of metal and FRP.