Real-time Bayesian inversion in resin transfer moulding using neural surrogates

Causon, M. E.; Iglesias, M. A.; Matveev, M. Y.; Endruweit, A.; Tretyakov, M. V.

Abstract

In Resin Transfer Moulding (RTM), local variations in reinforcement properties (porosity and permeability) and the formation of gaps along the reinforcement edges result in non-uniform resin flow patterns, which may cause defects in the produced composite component. The ensemble Kalman inversion (EKI) algorithm has previously been used to invert in-process data to estimate local reinforcement properties. However, implementation of this algorithm in some applications is limited by the requirement to run thousands of computationally expensive resin flow simulations. In this study, a machine learning approach is used to train a surrogate model which can emulate resin flow simulations near-instantaneously. A partition of the flow domain into a low-dimensional representation enables an artificial neural network (ANN) surrogate to make accurate predictions, with a simple architecture. When the ANN is integrated within the EKI algorithm, estimates for local reinforcement permeability and porosity can be achieved in real time, as was verified by virtual and lab experiments. Since EKI utilises the Bayesian framework, estimates are given within confidence intervals and statements can be made on-line regarding the probability of defects within sections of the reinforcement. The proposed framework has shown good predictive capabilities for the set of laboratory experiments and estimates for reinforcement properties were always computed within 1 s.

Citation

Causon, M. E., Iglesias, M. A., Matveev, M. Y., Endruweit, A., & Tretyakov, M. V. (2024). Real-time Bayesian inversion in resin transfer moulding using neural surrogates. Composites Part A: Applied Science and Manufacturing, 185, Article 108355. https://doi.org/10.1016/j.compositesa.2024.108355

• Journal Article Type: Article
• Acceptance Date: Jul 6, 2024
• Online Publication Date: Jul 19, 2024
• Publication Date: 2024-10
• Deposit Date: Jul 11, 2024
• Publicly Available Date: Jul 20, 2025
• Journal: Composites Part A: Applied Science and Manufacturing
• Print ISSN: 1359-835X
• Electronic ISSN: 1878-5840
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 185
• Article Number: 108355
• DOI: https://doi.org/10.1016/j.compositesa.2024.108355
• Keywords: B: Permeability; C: Computational modelling; C: Statistical properties/methods; E: Resin Transfer Moulding
• Public URL: https://nottingham-repository.worktribe.com/output/37153815
• Additional Information: This article is maintained by: Elsevier; Article Title: Real-time Bayesian inversion in resin transfer moulding using neural surrogates; Journal Title: Composites Part A: Applied Science and Manufacturing; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.compositesa.2024.108355; Content Type: article; Copyright: © 2024 The Author(s). Published by Elsevier Ltd.