crackdect 0.2

crack detection for composite materials

CrackDect

Expandable crack detection for composite materials.

This package provides crack detection algorithms for tunneling off axis cracks in glass fiber reinforced materials.

Full paper: CrackDect: Detecting crack densities in images of fiber-reinforced polymers
Full documentation: https://crackdect.readthedocs.io/en/latest/

If you use this package in publications, please cite the paper.

In this package, crack detection algorithms based on the works of Glud et al. [1] and Bender et al. [2] are implemented. This implementation is aimed to provide a modular "batteries included" package for this crack detection algorithms as well as a framework to preprocess image series to suite the prerequisites of the different crack detection algorithms.

Quick start

To install CrackDect, check at first the prerequisites of your python installation. Upon meeting all the criteria, the package can be installed with pip, or you can clone or download the repo. If the installed python version or certain necessary packages are not compatible we recommend the use of virtual environments by virtualenv or Conda.

Installation:

pip install crackdect

Prerequisites

This package is written and tested in Python 3.8. The following packages must be installed.

• scikit-image 0.18.1
• numpy 1.18.5
• scipy 1.6.0
• matplotlib 3.3.4
• sqlalchemy 1.3.23
• numba 0.52.0
• psutil 5.8.0

Motivation

Most algorithms and methods for scientific research are implemented as in-house code and not accessible for other researchers. Code rarely gets published and implementation details are often not included in papers presenting the results of these algorithms. Our motivation is to provide transparent and modular code with high level functions for crack detection in composite materials and the framework to efficiently apply it to experimental evaluations.

Contributing

Clone the repository and add changes to it. Test the changes and make a pull request.

Authors

• Matthias Drvoderic

License

This project is licensed under the MIT License.

References

[1] J.A. Glud, J.M. Dulieu-Barton, O.T. Thomsen, L.C.T. Overgaard Automated counting of off-axis tunnelling cracks using digital image processing Compos. Sci. Technol., 125 (2016), pp. 80-89

[2] Bender JJ, Bak BLV, Jensen SM, Lindgaard E. Effect of variable amplitude block loading on intralaminar crack initiation and propagation in multidirectional GFRP laminate Composites Part B: Engineering. 2021 Jul