Leiner, KathrinKathrinLeinerDollmann, Frederic P.Frederic P.DollmannHuber, MarcoMarcoHuberGeiger, ManuelManuelGeigerLeinberger, StefanStefanLeinberger2023-12-122023-12-122023https://publica.fraunhofer.de/handle/publica/45790910.1109/INDIN51400.2023.102182672-s2.0-85171128339Laser cutting is one of the classic methods used in metal processing. With increasing automation, it is important to ensure that large volumes can be produced reliably. This includes avoiding re-welding, known as cut interruption. In the presented work, audio signals are used to detect cut interruptions during laser cutting. The audio signal is classified into two classes: good cuts and cut interruptions. To solve this classification problem, the time series classifier RandOm Convolutional KErnel Transform (ROCKET) is used. The influence of the window size, the number of kernels and the repeatability of the training is investigated. With the presented work it is shown that a cut interruption detection with a microphone is possible. For a real world application there is a trade-off between accuracy and window size.enLaser CuttingMachine Learning ApplicationRidge ClassifierROCKETconference paper