• Authors: Alexander Hartmaier, Ronak Shoghi
• Organization: ICAMS, Ruhr University Bochum, Germany
• Contact: [email protected]

Finite Element Analysis (FEA) is a numerical method for studying mechanical behavior of fluids and solids. The pyLabFEA package introduces a simple version of FEA for solid mechanics and elastic-plastic materials, which is fully written in Python. Due to its simplicity, it is well-suited for teaching, and its flexibility in constitutive modeling of materials makes it a useful research tool.

The pyLabFEA package requires an Anaconda or Miniconda environment with a recent Python version. It can be installed directly from its GitHub repository with the following command

$ python -m pip install git+https://github.com/AHartmaier/pyLabFEA.git

Alternatively, the complete repository, including the source code, documentation and examples, can be cloned and installed locally. It is recommended to create a conda environment before installation. This can be done by the following the command line instructions

$ git clone https://github.com/AHartmaier/pyLabFEA.git ./pyLabFEA
$ cd pyLabFEA
$ conda env create -f environment.yml
$ conda activate pylabfea
$ python -m pip install . --user

The correct implementation can be tested with

$ pytest tests -v

After this, the package can be used within python, e.g. by importing the entire package with

import pylabfea as fea

Online documentation for pyLabFEA can be found under https://ahartmaier.github.io/pyLabFEA/. For offline use, open docs/index.html in your local copy to browse through the contents. The documentation has been generated using Sphinx.

pyLabFEA is conveniently used with Jupyter notebooks. Available notebooks with tutorials on linear and non-linear FEA, homogenization of elastic and elastic-plastic material behavior, and constitutive models based on machine learning algorithms are contained in the subfolder 'notebooks' of this repository and can be accessed via index.ipynb. An overview on the contents of the notebooks is also available here.

The Jupyter notebooks of the pyLabFEA tutorials are directly accessible on Binder

Python routines contained in the subfolder 'examples' of this repository demonstrate how ML flow rules can be trained based on reference materials with significant plastic anisotropy, as Hill or Barlat reference materials, but also for isotropic J2 plasticity. The training data consists of different stress tensors that mark the onset of plastic yielding of the material. It is important that these stress tensors cover the onset of plastic yielding in the full 6-dimensional stress space, including normal and shear stresses. The trained ML flow rules can be used in form of a user material (UMAT) for the commercial FEA package Abaqus.

Contributions to the pyLabFEA package are highly welcome, either in form of new notebooks with application examples or tutorials, or in form of new functionalities to the Python code. Furthermore, bug reports or any comments on possible improvements of the code or its documentation are greatly appreciated.

pyLabFEA requires the following packages as imports:

• NumPy for array handling
• Scipy for numerical solutions
• scikit-learn for machine learning algorithms
• MatPlotLib for graphical output
• pandas for data import
• fireworks direct import of data resulting from fireworks workflows

The pyLabFEA package comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to redistribute it under the conditions of the GNU General Public License (GPLv3)

The contents of the examples and notebooks are published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)