Abstract: A monitoring apparatus for an industrial manufacturing scenario is provided, including a) providing an initial version of a prediction unit, b) obtaining a set of unlabeled input data samples, c) aggregating an embedding indicator, d) aggregating a value indicator for each data sample of the set of unlabeled input data samples, e) selecting a subset of data samples of the set of unlabeled input data samples depending on the value indicator and outputting a labelling request to a labelling unit, f) receiving labels for the subset of data samples in the labelling unit, g) training the current version of the prediction unit resulting in a trained version of the prediction unit, and h) outputting a monitoring result for the set of unlabeled input data samples (Ds) by the trained version of the prediction unit indicating the quality of the supplemented manufacturing to control the manufacturing process.

Abstract: A monitoring apparatus and method for quality monitoring of a supplemented manufacturing process to a set of predefined manufacturing processes of industrial manufacturing includes: obtaining teacher models, providing an initial version of a student learning model and an initial version of a generator learning model, for each teacher model, training the generator learning model and a teacher specific student model to create data samples where the teacher model and teacher specific student learning models do not agree in their predictions, and adapting the current version of the student learning model based all trained teacher specific student models, customizing the adapted student model to the supplemented manufacturing process by training the adapted student model with annotated data of the supplemented manufacturing process, and monitoring the supplemented manufacturing process by processing the customized student model using data samples collected during the supplemented manufacturing process as input dat

Abstract: A method for quality control in industrial manufacturing for one or more production processes for producing at least one workpiece and/or product includes creating a learning model for at least one production process for the at least one workpiece and/or product. The learning model is trained and initialized using a meta-learning algorithm, and the learning model is calibrated using normalized data of the at least one production process for the at least one workpiece and/or product. Currently generated data of the at least one production process for at least one currently produced workpiece/product is forwarded to the learning model. The data is generated by sensors. The learning model compares the currently generated data with the normalized data and finds deviations. The learning model scales the deviations between the currently generated data and the normalized data, and the learning model communicates presence of an anomaly for the currently produced workpiece/product.