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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},
}
Metadata
Release files for finetune-am 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| finetune_am-0.0.1.tar.gz | 2.8 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| finetune_am-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.7 MB
Release files / finetune_am-0.0.1.tar.gz
| Download URL | finetune_am-0.0.1.tar.gz |
|---|---|
| Size | 2.8 MB |
| Tags | Source |
|
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Release files / finetune_am-0.0.1-py3-none-any.whl
| Download URL | finetune_am-0.0.1-py3-none-any.whl |
|---|---|
| Size | 2.8 MB |
| Tags | Python 3 |
|
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