Heider, MichaelDraude, ClaudeLange, MartinSick, Bernhard2019-08-272019-08-272019978-3-88579-689-3https://dl.gi.de/handle/20.500.12116/25088Additive Manufacturing machines following the Fused Deposition Modelling process can rapidly produce wide varieties of parts. A 3D computer model is divided into instructions the FDM machine uses to produce the part layer by layer. Numerous parameters can be modified to improve the instructions generated and extensive research is being performed into determining optimal parameters. Due to the complexity of the process and limited available data about influence factors, that might change over the duration of manufacturing, some produced parts have subpar quality or fail to be produced at all. An early automated detection that the resulting part will not be inside the preset quality tolerances could save substantial resources by not finishing production on those parts. Furthermore it might be possible to utilise machine learning techniques such as XCS to adaptively change instructions during printing as to return the part into the accepted parameter range.enAdditive ManufacturingMachine LearningQuality ControlText/Conference Paper10.18420/inf2019_ws521617-5468