Reference Dataset and Benchmark for Reconstructing Laser Parameters from On-axis Video in Powder Bed Fusion of Bulk Stainless Steel
This work addresses the need for standardized data and benchmarks in additive manufacturing, particularly for anomaly detection and process optimization, but it is incremental as it builds on existing monitoring techniques.
The authors tackled the problem of reconstructing laser parameters in powder bed fusion by creating RAISE-LPBF, a large dataset with on-axis video monitoring of 316L stainless steel, and provided baseline models and a benchmark for evaluation.
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.