CC BY-NC-ND 4.0Heymann, HenrikHenrikHeymannSchmitt, Robert H.Robert H.Schmitt2023-09-132024-04-262023-09-132023-05-02https://publica.fraunhofer.de/handle/publica/450635https://doi.org/10.24406/h-45063510.1016/j.procir.2023.03.05810.24406/h-4506352-s2.0-85164539766Predictive maintenance is an enabler of achieving a sustainable production in 3D printing processes. The goal of this paper is to answer the question how to realize predictive maintenance for Fused Deposition Modeling (FDM) of Polyether Ether Ketone (PEEK). In a real-life use case, aging effects of the 3D printer's nozzle have a direct negative impact on product quality but are not detectable from the outside. For this purpose, a machine learning pipeline is built including the integration and preparation of sensor data as well as the training of a regression model to predict the nozzle's remaining useful lifetime.en3D PrintingFDMMachine LearningPEEKPredictive MaintenanceFused Deposition ModelingPolyether Ether KetoneDDC::600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaftenjournal article