Data Access
Open repository
The open data related to the project are available on gitlab.uliege.be/moammm:
- Register on https://gitlab.com/users/sign_in (optional)
- Explore gitlab.uliege.be/moammm
Ongoing dataset generation
- Database of homogenised results, generated by simulation “Stochastic Volume Elements – (SVE)”: Contains stress-strain curved performed on PA12 lattice cells for different loading paths and geometrical parameters. The repository is regularly updated to include additional datasets
- Trained mesoscale surrogate model using full-field homogenisation from database of homogenised results of “Stochastic Volume Elements – (SVE)”: A recurrent neural network (RNN) is trained from the stress-strain curved performed on PA12 lattice cells for different loading paths and geometrical parameters. The RNN can interpolate stress-strain response for new cell geometrical parameters such as the struts radius.
Archived Data
- Data of « L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, « Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator« , http://dx.doi.org/10.5281/zenodo.7792804.
- C. Anglade, L. Cobian, M. Monclus, F. Karayagiz, M. Mustafa, L. Noels. « Finite-strain Visco-elastic Visco-plastic model identification of PA12 material printed along different directions », http://dx.doi.org/10.5281/zenodo.6903647.
- Data of » L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus. Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behavior Polymer Testing (2022): 107556« , http://dx.doi.org/10.5281/zenodo.6136935
- Data of « L. Wu, and L. Noels. Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step. Computer Methods in Applied Mechanics and Engineering (2022): 114476.« , http://dx.doi.org/10.5281/zenodo.5668390
- Data of « L. Wu, V. D. Nguyen, N. G. Kilingar, and L. Noels. A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths. Computer Methods in Applied Mechanics and Engineering (2020): 113234. « , https://dx.doi.org/10.5281/zenodo.3902663
Data are Licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0)