Towards continual learning with the artificial neural twin applied to recycling processes - Mendez, Ronald; Maier, Andreas; Emmert, Johannes (Optical Characterization of Materials, 2025)
With the increasing use of AI models in industrial processes, comes the need for more training data. Further, material packaging industry is an ever evolving sector, which makes the task of the AI models used by post-consumer package sorting facilities equally dynamic, rapidly outdating the training data, and requiring the generation of new expensive datasets. We propose to apply continual learning in combination with the Artificial Neural Twin (ANT), to continually train models without generating new data manually. We initially train with a small dataset, then, apply Orthogonal Weight Modification with training stimuli from quality control measurements collected by the ANT, and poof through experiments that this can replace the expensive process of dataset generation.
CRIS-Team Scopus Importer:2025-05-23
Keywords: artificial neural twin; backpropagation; Continual learning; orthogonal weight modification; process optimization