# Reference Dataset and Benchmark for Reconstructing Laser Parameters from On-axis Video in Powder Bed Fusion of Bulk Stainless Steel

arXiv: https://arxiv.org/abs/2409.12475 · HTML: https://mlsanity.com/paper/2409.12475/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding](https://mlsanity.com/paper/2304.06906/) (2023)
- [Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learning](https://mlsanity.com/paper/2112.11212/) (2021)

## Built on by (most cited first, 2 of 2)

- [Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems](https://mlsanity.com/paper/2412.02729/) (2024)
- [Optimizing edge AI models on HPC systems with the edge in the loop](https://mlsanity.com/paper/2505.19995/) (2025)

## Abstract (arXiv)

We present RAISE-LPBF, a large dataset on the effect of laser power and laser dot speed in powder bed fusion (LPBF) of 316L stainless steel bulk material, monitored by on-axis 20k FPS video. Both process parameters are independently sampled for each scan line from a continuous distribution, so interactions of different parameter choices can be investigated. The data can be used to derive statistical properties of LPBF, as well as to build anomaly detectors. We provide example source code for loading the data, baseline machine learning models and results, and a public benchmark to evaluate predictive models.