https://github.com/lululxvi/deep-learning-for-indentation
Extraction of mechanical properties of materials through deep learning from instrumented indentation
https://github.com/lululxvi/deep-learning-for-indentation
additive-manufacturing deep-learning indentation mechanics-of-materials
Last synced: 5 months ago
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Extraction of mechanical properties of materials through deep learning from instrumented indentation
- Host: GitHub
- URL: https://github.com/lululxvi/deep-learning-for-indentation
- Owner: lululxvi
- License: apache-2.0
- Created: 2020-02-10T22:25:01.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-03-16T20:10:05.000Z (over 3 years ago)
- Last Synced: 2025-04-08T14:41:46.640Z (6 months ago)
- Topics: additive-manufacturing, deep-learning, indentation, mechanics-of-materials
- Language: Python
- Size: 94.7 KB
- Stars: 64
- Watchers: 7
- Forks: 31
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Extraction of mechanical properties of materials through deep learning from instrumented indentation
The data and code for the paper [L. Lu, M. Dao, P. Kumar, U. Ramamurty, G. E. Karniadakis, & S. Suresh. Extraction of mechanical properties of materials through deep learning from instrumented indentation. *Proceedings of the National Academy of Sciences*, 117(13), 7052-7062, 2020](https://www.pnas.org/content/early/2020/03/13/1922210117).
## Data
All the data is in the folder [data](data).
## Code
All the code is in the folder [src](src). The code depends on the deep learning package [DeepXDE](https://github.com/lululxvi/deepxde) v1.1.2. If you use DeepXDE>1.1.2, you need to set `standardize=True` in `dde.data.MfDataSet()`.
- [data.py](src/data.py): The classes are used to read the data file. Remember to uncomment certain line in `ExpData` to scale `dP/dh`.
- [nn.py](src/nn.py): The main functions of multi-fidelity neural networks.
- [model.py](src/model.py): The fitting function method. Some parameters are hard-coded in the code, and you should modify them for different cases.
- [fit_n.py](src/fit_n.py): Fit strain-hardening exponent.
- [mfgp.py](src/mfgp.py): Multi-fidelity Gaussian process regression.
## Cite this work
If you use this code for academic research, you are encouraged to cite the following paper:
```
@article{Lu7052,
author = {Lu, Lu and Dao, Ming and Kumar, Punit and Ramamurty, Upadrasta and Karniadakis, George Em and Suresh, Subra},
title = {Extraction of mechanical properties of materials through deep learning from instrumented indentation},
volume = {117},
number = {13},
pages = {7052--7062},
year = {2020},
doi = {10.1073/pnas.1922210117},
journal = {Proceedings of the National Academy of Sciences}
}
```
## Questions
To get help on how to use the data or code, simply open an issue in the GitHub "Issues" section.