Active transfer learning for data-driven manufacturing process modelling

نویسندگان

چکیده

Manufacturing process modelling (MPM) aims to construct high-fidelity digital predictive models of the concerned properties products, processes or manufacturing systems for further optimisation and improvement activities. Data-driven methods, including machine learning deep learning, have drawn immense attention MPM problems because their powerful representative ability. However, labelled data concerning in is often insufficient sparse expensive time-consuming experiments simulations. The scarcity hinders development data-driven problems. This paper proposes an active transfer framework by integrating generation processing relevant reduce requirements data. Firstly, initial labelling module introduces a more informative dataset rather than randomly generated one. Then, model can extract general information from address target task. Besides, iterative determine query promising new according performance current model. effectiveness proposed verified tool wear prediction case. experimental outcomes demonstrate that three modules enhance data-drive under limited

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ژورنال

عنوان ژورنال: Procedia CIRP

سال: 2023

ISSN: ['2212-8271']

DOI: https://doi.org/10.1016/j.procir.2023.06.018