Digital twin and cross-scale mechanical interaction for fabric rubber composites considering model uncertainties

Xiaoyao Xu, Guowen Wang, Shanyong Xuan, Yimeng Shan, Heng Yang, Xuefeng Yao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Digital twin ushers in growth as one cutting-edge technology that enables high-precision, informative and real-time interaction of advanced composites, influencing the paradigm of composite analysis and design in aerospace, automotive, intelligent electronics and other fields. The online and fine evolution of digital twins at scale face challenges due to creating scalable predictions, updates, and controls with coupled information in workflows. In this work, an interactive digital twin methodology for complex composite structures (CCSDT) based on machine learning is introduced. To demonstrate the framework's feasibility and applicability, a hybrid architecture comprising hierarchical deep neural networks (H-DNNs), statistical inference and cross-scale physical constraints is proposed considering the fabric rubber composites (FRC) in aerospace field. The framework predicts 3D displacement and stress fields directly from sensing features and can incorporate dynamic updating and evaluation of computational models into data assimilation. Real-time prediction and uncertainty quantification are verified through synthetic and experimental data, and the capability of CCSDT in perception and decision is demonstrated by a case of cruise state monitoring. The comparison between the measured strain field and the post-processing predicted strain field shows the extensibility of the direct prediction results. These results provide guidance for the development of composite digital twins, stimulating the potential for cost-effective and efficient digital twin services.

Original languageEnglish
Article number110431
JournalComposites Science and Technology
Volume248
DOIs
Publication statusPublished - 22 Mar 2024

Keywords

  • Digital twin
  • Fabric rubber composites
  • Gaussian process regression
  • Machine learning
  • Mechanical properties

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