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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

Acknowledgements

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

License

MIT