According to the email confirmed by the organiser of CIRP ICME '27, our paper entitled, "In-process tool incidence identification based on temporal pyramid pooling and convolutional neural network", gets accepted.
Adaptive drilling allows for the change of cutting parameters when drilling multi-material stacks, for example carbon fibre reinforced polymer (CFRP) combined with Al which are commonly found in the aerospace industry. This work proposes a deep learning approach to identify process incidences from signals acquired whilst drilling CFRP/Al stacks, which can achieve both high accuracy and immediate response by a single set of parameters. The influence of both sample length and frequency on the model’s classification performance is quantified and investigated. This work makes a notable contribution to improving the accuracy and speed of decision making for identifying incidences based on which changes in cutting parameters can be triggered in adaptive drilling of aerospace stacks.
We made a special page for this paper to demonstrate its details. If you feel interested, please visit TPP project