Schmitt, RobertRobertSchmittWolfschläger, D.D.WolfschlägerMasliankova, E.E.MasliankovaMontavon, B.B.Montavon2022-12-072022-12-072022https://publica.fraunhofer.de/handle/publica/42968610.1016/j.cirp.2022.03.0162-s2.0-85128202181Deep Learning (DL) is leveraged in a growing number of industrial applications. One strength is the data-driven ability to extract characteristic features from complex inputs in form of a latent vector without the need for closed formulation or derivation from a priori known quantities. This work proposes a framework based on generative DL methods to interpret these latent vectors as metrological quantities. The approach is explored in the machine vision domain by implementing a model utilising style-based adversarial latent autoencoders, principal component analysis, and logistic regression. It is successfully evaluated on an industrial image set of aluminium die casting surfaces.enArtificial IntelligenceMetrologySurfaceanalysisjournal article