According to the email confirmed by the organiser of CIE 50, our paper entitled, "In-process process incidence identification in drilling of aerospace stacks", gets accepted.
Adaptive drilling enables the optimisation of cutting parameters to suit the specific requirements of multi-material stacks, for example hybrid stacks comprising carbon fibre reinforced polymer (CFRP) and aluminium which are commonly used in the aerospace industry. This work proposed a deep learning approach to identify process incidences from different signals recorded during the drilling of CFRP/Al stacks. The influence of machining signals on the model’s classification performance is evaluated and investigated. This work contributes to improving the accuracy and reliability with which key incidences during a stack drilling cycle can be recognized in order to adjust cutting parameters during the drilling process to enable adaptive drilling in aerospace stacks.