CC BY-NC-ND 4.0Sarishvili, AlexAlexSarishviliJust, DanielDanielJustMoser, KevinKevinMoserWirsen, AndreasAndreasWirsenDiemert, JanJanDiemertJirstrand, MatsMatsJirstrand2022-03-0619.11.20212021https://publica.fraunhofer.de/handle/publica/27086310.1002/cite.202100093Continuous plastics compounding processes are highly complex from a physicochemical point of view and correspondingly difficult to optimize. Model-based digital twins suitable for this purpose require complex simulation approaches, trained personnel and input variables that are difficult to determine. However, data-based digital twins, which are in principle suitable for this purpose, often fail because of the enormous trial effort which is required to generate a sufficiently large database. To overcome this problem this paper describes a hybrid approach for generating the necessary database for a data-based digital twin. By intelligent combination of real experiments, adaptation of the physical-chemical process model to these few experimental data and subsequent data cloud generation with the adapted process model results in a sufficient data base for the digital twin.endigital twinmachine learningPlastic Extrusion003660660006519journal article