RAISE-LPBF-Laser benchmark repo

Official repository of the RAISE-LPBF-Laser benchmark.

We hereby provide PyTorch code to load the dataset as well as the models we used as baselines.

More information can be found on our website makebench.eu.

Datasets

A collection of PyTorch Datasets can be found in the dataset.py file:

• FramesSP: extracts frames for training/testing given the filepath to a RAISE-LPBF-Laser HDF5 dataset as downloaded from the website;
• OneWaySP: inherits from FramesSP to further preprocess the frames for compatibility with video recognition models such as 3DResnet, X3D, MViT, etc.;
• TwoWaysSP: inherits from FramesSP to further preprocess the frames for compatibility with two-way model SlowFast.

This is just a baseline to demonstrate how to use the data; doubtlessly it is possible to achieve better performance. We encourage everyone to submit results of improved models or preprocessing methods to Makebench.eu.

Models

A collection of models as PyTorch Modules can be found in the models/ folder:

• CNN3DResnet: Hara et Al. (2017). Learning spatio-temporal features with 3D residual networks for action recognition.
• CNN3DSlowFast: Feichtenhofer et Al. (2019). SlowFast Networks for Video Recognition.
• MViT: Fan et Al. (2021). Multiscale Vision Transformers.
• Swin3D: Yang et Al. (2023). Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding.
• ViViT: Arnab et Al. (2021). ViViT: A Video Vision Transformer.
• X3D: Feichtenhofer et Al. (2020). X3D: Expanding Architectures for Efficient Video Recognition.

Citation

@article{BLANC2023100161,
title = {Reference dataset and benchmark for reconstructing laser parameters from on-axis video in powder bed fusion of bulk stainless steel},
journal = {Additive Manufacturing Letters},
volume = {7},
pages = {100161},
year = {2023},
issn = {2772-3690},
doi = {https://doi.org/10.1016/j.addlet.2023.100161},
url = {https://www.sciencedirect.com/science/article/pii/S2772369023000427},
author = {Cyril Blanc and Ayyoub Ahar and Kurt {De Grave}},
keywords = {Selective laser melting, Stainless steel, On-axis camera, Dataset, Machine learning, Monitoring}
}