Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
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.
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 language | English |
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Title of host publication | ECCM21 - Proceedings of the 21st European Conference on Composite Materials |
Publisher | European Society for Composite Materials (ESCM) |
Pages | 1252-1259 |
Number of pages | 8 |
Volume | 3 |
ISBN (print) | 978-2-912985-01-9 |
Publication status | Published - 2 Jul 2024 |
Peer-reviewed | Yes |
Conference
Title | 21st European Conference on Composite Materials |
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Abbreviated title | ECCM 21 |
Conference number | 21 |
Duration | 2 - 5 July 2024 |
Website | |
Degree of recognition | International event |
Location | La Cité Nantes Congress Centre |
City | Nantes |
Country | France |
External IDs
ORCID | /0000-0002-0169-8602/work/162844831 |
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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