Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation


Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.

Publication DOI: https://doi.org/10.1016/j.rcim.2023.102545
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Additional Information: Copyright © 2023 Elsevier Ltd. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License [https://creativecommons.org/licenses/by-nc-nd/4.0/]. Acknowledgments & Funding: Funded by the Deutsche Forschungsgemeinschaft, Germany (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.
Uncontrolled Keywords: Artificial intelligence,Digital shadow,Digital twin,Machine tool,Smart manufacturing,Control and Systems Engineering,Software,Mathematics(all),Computer Science Applications,Industrial and Manufacturing Engineering
Publication ISSN: 0736-5845
Last Modified: 20 Jun 2024 07:23
Date Deposited: 01 Mar 2023 17:22
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Related URLs: https://www.sci ... 736584523000212 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-08
Published Online Date: 2023-02-20
Accepted Date: 2023-02-10
Authors: Huang, Ziqi
Fey, Marcel
Liu, Chao (ORCID Profile 0000-0001-7261-3832)
Beysel, Ege
Xu, Xun
Brecher, Christian

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