Meyes, RichardRichardMeyesTercan, HasanHasanTercanMeisen, TobiasTobiasMeisen2023-05-162023-05-162019978-3-86309-662-5https://fis.uni-bamberg.de/handle/uniba/58607Deep Learning (DL), Artificial Intelligence (AI), Machine Learning (ML): Three terms, often used synonymously, that stand for a new kind of intelligent systems. Companies worldwide invest financial and human resources to tap the potential and promises of these technologies for themselves: be it in the establishment of data science departments or of powerful computer clusters. The automotive industry is no exception – thereby, with a prominent media focus on “autonomous driving”. However, this is not the only application area for Artificial Intelligence in the automotive domain. The use of machine learning is also researched and applied in automotive production plants: From the use in the body shop all the way to predictive estimations of what proportion of a component is damaged. In this contribution, we discuss the use of Artificial Intelligence in practical examples of automotive production and point out which challenges exist and which approaches are promising. At the same time, we discuss and evaluate the potentials and challenges.engIndustrial Machine LearningAutomotivePredictive QualityFailure ForecastingTime Series DataSensor AnalysisSoft Sensors650conferenceobject