| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Gerritzen, Johannes
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 displayed)
- 2024Graph based process models as basis for efficient data driven surrogates - Expediting the material development process
- 2024A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRPcitations
- 2023Modelling delamination in fibre-reinforced composites subjected to through-thickness compression by an adapted cohesive law
- 2023Development and verification of a cure-dependent visco-thermo-elastic simulation model for predicting the process-induced surface waviness of continuous fiber reinforced thermosetscitations
- 2023Modelling of composite manufacturing processes incorporating large fibre deformations and process parameter interactions
- 2022A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameterscitations
- 2022Development of a high-fidelity framework to describe the process-dependent viscoelasticity of a fast-curing epoxy matrix resin including testing, modelling, calibration and validationcitations
- 2021Contribution to Digital Linked Development, Manufacturing and Quality Assurance Processes for Metal-Composite Lightweight Structurescitations
- 2020Robust development, validation and manufacturing processes for hybrid metal-composite lightweight structures
Places of action
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article
A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP
Abstract
<p>In this work, we demonstrate how Machine Learning (ML) techniques can be employed to externalize the knowledge and time intensive process of material parameter identification. This is done on the example of a recent data driven material model for the non-linear shear behavior of glass fiber reinforced polypropylene (GF/PP) (Gerritzen, 2022). A convolutional neural network (CNN) based model architecture is trained to predict material modeling parameters based on the input of stress–strain-curves. The optimal model architecture and training setup are determined by hyperparameter optimization (HPO). Solely virtual data, generated using the target material model, is used throughout the training and HPO. The final CNN is capable of calculating model parameter combinations from experimental stress–strain-curves which lead to excellent agreement between experimental and associated model curve.</p>