This project is a proof of concept on how ML can be used to find critical parts for AM. Our new approach allows to easily separate input meshes in critical and non critical parts. For training artificial data is created.
pip3 install trimesh open3d networkx scipy pandas numpy
apt get install openscad (on Debian based systems)
data_gen.py generates artificial trainingdata
train.py trains a model on the generated data
val.py can be used to visualize how the model performs on validation meshes
split.py splits input meshes into critical and non critical meshes depending on which faces are critical