Toward a self-adaptive digital twin based Active learning method: an application to the lumber industry

Abstract

Digital Twins (DT) is an extremely promising framework developed in the context of Industry 4.0 to facilitate the convergence of the physical and digital spaces. Numerous challenges remain, however, in terms of development, deployment, and self-adaptability of the DT faced with changes from its physical twin. Concerning this last point in particular, the set of Machine Learning (ML) methods known as Active Learning appears promising. This framework allows the DT to play an active role in the selection of the data samples used to train supervised ML models. This paper proposes a use-case inspired from the sawmill industry to illustrate the interest of these method in the presence of various changes in the flow of data gathered by the DT.