=========================================================== PyMDP: A decomposition tool for multi-direction 3D printing

.. image:: /img/figRepo.jpg

Multi-directional 3D printing by robotic arms or multi-axis systems is a new way of manufacturing. As a strong complementary of layer-wise additive manufacturing, multi-directional printing has the capability of decreasing or eliminating the need for support structures.

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Notice

This library is no longer actively maintained. If you come across any complications during the compilation process, we suggest exploring the option of using a previous version of VCPKG from the year 2020. This approach has proven to be effective for numerous users who encountered similar issues and reached out to us via email.

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Dependency

Eigen <http://eigen.tuxfamily.org/>_ CGAL <https://www.cgal.org/>_ PyBind11 <http://github.com/pybind/pybind11/>_

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Install

We use CMake (>=3.16) and vcpkg to facilate the compilation process. You can download and install CMake from their official website, and install vcpkg by

.. code-block:: bash

git clone https://github.com/Microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh
./vcpkg integrate install

Next, you will need to install CGAL dependency:

.. code-block:: bash

vcpkg install eigen3
vcpkg install cgal

Note: If you are using a Windows system, please be aware that vcpkg will install the 32-bit package as the default option. If you encounter this situation, you may need to utilize the following command.

.. code-block:: bash

vcpkg install eigen3:x64-windows
vcpkg install cgal:x64-windows

Then you can easily install the library by using the following command.

.. code-block:: bash

pip install . --install-option="--vcpkg=YOUR_VCPKG_FOLDER"

Please change "YOUR_VCPKG_FOLDER" to the folder where VCPKG is installed.

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Demo

.. code-block:: python

from pymdp import BGS

if __name__ == "__main__":
    proc = BGS(filename='kitten.off')
    proc.set_beam_width(10)
    proc.set_output_folder('kitten')
    proc.start_search()

We have recently introduced a learning-based approach to enhance the original search algorithm, utilizing learning-to-rank techniques. The source codes for this method can be found in the "learning_based.py" file, which is available for access.

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Credits

We kindly request that any scientific publications utilizing PyMDP cite our work, as we greatly appreciate your support.

.. code-block:: bibtex

@inproceedings{wu2017robofdm,
  title={RoboFDM: A robotic system for support-free fabrication using FDM},
  author={Wu, Chenming and Dai, Chengkai and Fang, Guoxin and Liu, Yong-Jin and Wang, Charlie CL},
  booktitle={2017 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={1175--1180},
  year={2017},
  organization={IEEE}
}

.. code-block:: bibtex

@article{wu2019general,
title={General Support-Effective Decomposition for Multi-Directional 3-D Printing},
author={Wu, Chenming and Dai, Chengkai and Fang, Guoxin and Liu, Yong-Jin and Wang, Charlie CL},
journal={IEEE Transactions on Automation Science and Engineering},
year={2019},
publisher={IEEE}
}

.. code-block:: bibtex

@article{wu2020learning,
  title={Learning to accelerate decomposition for multi-directional 3D printing},
  author={Wu, Chenming and Liu, Yong-Jin and Wang, Charlie CL},
  journal={IEEE Robotics and Automation Letters},
  volume={5},
  number={4},
  pages={5897--5904},
  year={2020},
  publisher={IEEE}
}

In our learning-to-accelerate work, we use urank <https://github.com/XiaofengZhu/uRank_uMart>_ impelementation provided by Xiaofeng Zhu. Please consider cite their work if you also found it helpful.