WITHDRAWN: Theoretical Analyses and Performance Comparison of Physics-Based, Data-Driven, and Hybrid Models for Digital Twin Applications in Manufacturing

doi:10.21203/rs.3.rs-7807793/v1

WITHDRAWN: Theoretical Analyses and Performance Comparison of Physics-Based, Data-Driven, and Hybrid Models for Digital Twin Applications in Manufacturing

2025 · doi:10.21203/rs.3.rs-7807793/v1

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Abstract

Abstract Digital twins (DT) are increasingly important in modern manufacturing systems to extend the physical system’s capabilities and improve manufacturing key performance indicators. Nevertheless, selecting the most effective modeling approach for DT services remains challenging. While physics-based models (PBM), data-driven models (DDM), and hybrid models are all utilized within DT implementations, there is a lack of theoretical understanding regarding their comparative performance under equivalent model input conditions. This research addresses this gap by developing a structured theoretical framework that relates model quality to key input resources: domain knowledge, modeling expertise, and data quality. The framework proposes that hybrid models can systematically outperform both PBM and DDM by optimizing the knowledge-data trade-off through the synergistic integration of physical principles with pattern recognition capabilities. To validate this theoretical framework, we implement and compare all three modeling approaches in an industrial stoneware floor tile polishing case study. Results reveal a performance hierarchy: the hybrid model (R² ≈ 0.55) significantly outperforms both the pure DDM approach (R² ≈ 0.40) and the PBM (R² ≈ 0.08) while providing superior transferability to different process conditions. The case study confirms that hybrid models can extract more value from identical input resources than either pure modeling paradigm alone. In addition to such comparisons, this research provides manufacturers with practical guidance for selecting appropriate modeling strategies in DT implementations, particularly valuable in scenarios with limited data or incomplete physical understanding of complex processes.

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P. Sousa, Matthias Klar, Jan C. Aurich This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7807793/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Editorial Note The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author. Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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