Additive Manufacturing related tools
This project has been archived.
The maintainers of this project have marked this project as archived. No new releases are expected.
Project description
finetune-am
Additive Manufacturing related software modules
Getting Started
- Installation
pip install am
- Create a new
Workspace:
am create_workspace test
- Navigate to workspace directory and
manage.pyfile
python manage.py
-
Drop
.gcodefile inpartsfolder within workspace directory. -
Select and parse
.gcodefile withinpartsfolder with.
python manage.py parse_gcode
- Create solver.
python manage.py create_solver model="eagar-tsai" name="eagar-tsai" device="cuda:0"
- Create simulation .
python manage.py create_simulation
- Run simulation .
python manage.py run_simulation layer_index="99"
References
@article{
title = {Thermal control of laser powder bed fusion using deep reinforcement learning},
volume = {46},
issn = {2214-8604},
url = {https://www.sciencedirect.com/science/article/pii/S2214860421001986},
doi = {10.1016/j.addma.2021.102033},
journal = {Additive Manufacturing},
author = {Ogoke, Francis and Farimani, Amir Barati},
month = oct,
year = {2021},
keywords = {Additive Manufacturing, Deep Reinforcement Learning, Powder Bed Fusion},
pages = {102033},
}
@phdthesis{
address = {United States -- California},
type = {Ph.{D}.},
title = {Physics-{Based} {Surrogate} {Modeling} of {Laser} {Powder} {Bed} {Fusion} {Additive} {Manufacturing}},
copyright = {Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.},
url = {https://www.proquest.com/docview/2503475225/abstract/71F47FA688874C02PQ/1},
language = {English},
school = {University of California, Davis},
author = {Wolfer, Alexander James},
year = {2020},
note = {ISBN: 9798582546023},
keywords = {Additive Manufacturing, Bayesian statistics, Mechanical engineering, Powder bed fusion, Surrogate model, Uncertainty quantification},
}
@article{
title = {Fast solution strategy for transient heat conduction for arbitrary scan paths in additive manufacturing},
volume = {30},
issn = {2214-8604},
url = {https://www.sciencedirect.com/science/article/pii/S2214860419303446},
doi = {10.1016/j.addma.2019.100898},
journal = {Additive Manufacturing},
author = {Wolfer, Alexander J. and Aires, Jeremy and Wheeler, Kevin and Delplanque, Jean-Pierre and Rubenchik, Alexander and Anderson, Andy and Khairallah, Saad},
month = dec,
year = {2019},
keywords = {Additive manufacturing, Gaussian convolution, Heat conduction, Powder bed fusion, Semi-analytical model},
pages = {100898},
}
@article{
title = {The {Theory} of {Moving} {Sources} of {Heat} and {Its} {Application} to {Metal} {Treatments}},
volume = {68},
issn = {0097-6822},
url = {https://doi.org/10.1115/1.4018624},
doi = {10.1115/1.4018624},
number = {8},
journal = {Transactions of the American Society of Mechanical Engineers},
author = {Rosenthal, D.},
month = dec,
year = {2022},
pages = {849--865},
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file finetune_am-0.0.1.tar.gz.
File metadata
- Download URL: finetune_am-0.0.1.tar.gz
- Upload date:
- Size: 2.8 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: python-httpx/0.28.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
915960838159781be8f877a01a8fa0d0a111c10e44faf824e25a0a50618256a2
|
|
| MD5 |
adf882066577cd7fd93f464aab2d87cc
|
|
| BLAKE2b-256 |
f338086a72178e9e0c4ae72d52cf34122fbe7f59ffdd6db07b803ccdf1cee834
|
File details
Details for the file finetune_am-0.0.1-py3-none-any.whl.
File metadata
- Download URL: finetune_am-0.0.1-py3-none-any.whl
- Upload date:
- Size: 2.8 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: python-httpx/0.28.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
51905dace1be2f3c9bffab169f4e6dc4cb8a033bf8cab831a9ac55aab9850f1e
|
|
| MD5 |
0d28e3a7f226803977914d8255bd1e11
|
|
| BLAKE2b-256 |
26fb0701eabd4d49eaeda95c15b6793d446666e5babbad475b57a925c1dfdcf6
|