ProSi-3D: Robust and reliable process monitoring for Laser Powder Bed Fusion

This repo contains the source for a adaptable and configurable process monotoring system, (mostly) based on Python. We also supply config files to run the package within a Docker container.

Installation

Requirements

The package deps are all listed in a .yml File in the docker-env/ directory. To install them using conda:

conda create --name prosi3d --file docker-env/environment.yml

Using tox

For now, you can use the package by invoking the tox build system:

tox -e build

Using setuptools

Alternatively, you can use the Python package setuptools to build the local package:

python setup.py develop

Using Docker

Lastly, you can build and run the Docker container that comes along with the package:

docker build . -t prosi3d:latest

Run the container in a terminal:

docker run --rm -ti prosi3d:latest

Usage/Examples

After Installation, you can import and use the package in Python. Say, you want to process a data stream that comprises several acoustic sensors. You can then read the data from a HDF file and create their respective features easily and uniformly:

from prosi3d.sensors.acousticair import Accousticair
from prosi3d.sensors.acousticplatform import Accousticplatform
from prosi3d.sensors.recoater import Recoater

def test_analysis_loop():
    hdf_name = 'data/ch4raw_00593.h5'

    acc = Accousticair()
    acc_p = Accousticplatform()
    acc_r = Recoater()

    sensors = [acc,acc_p,acc_r]

    for sensor in sensors:
        sensor.get_data(hdf_name)
        sensor.process()
        sensor.plot_test()
        sensor.write()

Contributing

Contributions are always welcome!

See contributing.md for ways to get started.

Please adhere to this project's code of conduct.

Authors

• @pzimbrod
• @bjoernri
• @anna-lenastolze

Acknowledgements

This project is kindly funded by the German Bavarian Ministry of Economic Affairs, Regional Development and Energy.

License

MIT