Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Abstract

Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior. To capture this in simulations, intricate models with a variety of parameters are typically used. The identification of values for such parameters is highly challenging and requires in depth understanding of the model itself. Machine learning (ML) is a promising approach for alleviating this challenge by directly predicting parameters based on experimental results. So far, this works mostly for purely artificial data. In this work, two approaches to generalize to experimental data are investigated: a sequential approach, leveraging understanding of the constitutive model and a direct, purely data driven approach. This is exemplary carried out for a highly non-linear strain rate dependent constitutive model for the shear behavior of FRP.
The sequential model is found to work better on both artificial and experimental data. It is capable of extracting well suited parameters from the artificial data under realistic conditions. For the experimental data, the model performance depends on the composition of the experimental curves, varying between excellently suiting and reasonable predictions. Taking the expert knowledge into account for ML-model training led to far better results than the purely data driven approach. Robustifying the model predictions on experimental data promises further improvement.

Details

Original languageEnglish
Title of host publicationECCM21 - Proceedings of the 21st European Conference on Composite Materials
PublisherEuropean Society for Composite Materials (ESCM)
Pages1252-1259
Number of pages8
Volume3
ISBN (print)978-2-912985-01-9
Publication statusPublished - 2 Jul 2024
Peer-reviewedYes

Conference

Title21st European Conference on Composite Materials
Abbreviated titleECCM 21
Conference number21
Duration2 - 5 July 2024
Website
Degree of recognitionInternational event
LocationLa Cité Nantes Congress Centre
CityNantes
CountryFrance

External IDs

ORCID /0000-0002-0169-8602/work/162844831
ORCID /0000-0003-1370-064X/work/162844928
ORCID /0000-0003-2653-7546/work/162845447

Keywords

Keywords

  • Direct parameter identification, Machine learning, Convolutional neural networks, Strain rate dependency, Fiber reinforced plastics