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

TL;DR

This paper introduces RAISE-LPBF, a comprehensive dataset with on-axis high-speed video capturing laser parameters in powder bed fusion of stainless steel, enabling analysis and benchmarking of process modeling and anomaly detection.

Contribution

It provides a large, detailed dataset with independent sampling of laser parameters, along with baseline models and a public benchmark for the LPBF process.

Findings

Dataset enables statistical analysis of LPBF

Baseline machine learning models established

Benchmark facilitates model evaluation and comparison

Abstract

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.