Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methods

Christos Nastos, Panagiotis Komninos, Dimitrios Zarouchas*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
48 Downloads (Pure)

Abstract

A hybrid methodology based on numerical and non-destructive experimental schemes, which is able to predict the structural level strength of composite laminates is proposed on the current work. The main objective is to predict the strength by substituting the up to failure experiments with non-destructive experiments where the investigated specimen is loaded up to 20% of its maximum load. A significant gap exists between the 20% and the 100% load which is proposed to be treated by high fidelity physics-based numerical models, deep learning techniques, and non-catastrophic experiments. Thus, a deep learning algorithm is developed, based on the convolutional neural networks and trained by probabilistic failure analysis datasets which result from the utilization of the stochastic finite element method. Also, the Monte Carlo dropout technique is embedded into the developed convolutional neural network to estimate the uncertainty induced by the investigated variations between the simulated and experimental data. The current paper provides a thorough description of the proposed methodology and a practical example which demonstrates the validity of the method.

Original languageEnglish
Article number116815
Number of pages19
JournalComposite Structures
Volume311
DOIs
Publication statusPublished - 2023

Keywords

  • Composite structures
  • Deep learning
  • Probabilistic models
  • Stochastic finite element method
  • Strength prediction
  • Uncertainty

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