Virtual data-driven optimisation for zero defect composites manufacture

Yi Wang*, Siyuan Chen, Iryna Tretiak, Stephen R Hallett, Jonathan P Belnoue

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)

Abstract

Manufacture-induced defects are a critical issue in composites applications that result in high volumes of material waste and costly experimental trials to help mitigate this. Advances in process modelling techniques have enabled the prediction of defects. However, the high computational cost of these tools limits their usefulness in an industrial context as they often struggle with the ever-increasing size of industrial structures, and present significant challenges to undertaking large optimisation problems. In this work, a recently proposed homogenisation scheme is used to accurately simulate the autoclave processing of a composite part of industrial complexity in a fraction of the time of other state-of-the-art processes. Physical manufacture of a demonstrator part is used to evaluate the model in terms of accuracy. Based on the observed good agreement (discrepancy of less than 1.5% in the thicker sections and less than 7% in the thinner region of the part), a data-driven optimisation of the part’s caul plate has been conducted that aims to mitigate defects whilst keeping cost and material use low. Further manufacturing trials validate the proposed framework by demonstrating successful defect elimination and a 25% improvement in the maximum local deviation from ideal design.
Original languageEnglish
Article number112934
Number of pages14
JournalMaterials and Design
Volume241
Early online date12 Apr 2024
DOIs
Publication statusPublished - 1 May 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Research Groups and Themes

  • Bristol Composites Institute ACCIS

Keywords

  • Composites 4.0
  • Machine Learning
  • Zero Defect
  • Consolidation
  • Tooling

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