Metrologically interpretable feature extraction for industrial machine vision using generative deep learning

Deep 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. (C) 2022 CIRP. Published by Elsevier Ltd. All rights reserved.