Melt-Pool-Kinetics: A multi-source melt pool compilation for vision-based analytics applications in additive manufacturing

Additive manufacturing (AM) is a technique used to fabricate physical objects by layering materials from a 3D digital model. Melt pool represents the region of superheated molten material in metallic AM. Melt pool monitoring in AM processes has been shown to be effective for a range of downstream analytics tasks. However, metallic AM processes are characterized by a wide variety of process parameters, materials, and monitoring conditions. This diversity has driven the need for a comprehensive dataset that can standardize melt pool image data across process, monitoring, and application contexts. To this end, the current work presents a diverse collection of melt pool images sourced from a range of process monitoring applications. The dataset is systematically collected from research institutions, government organizations, scientific articles, and open databases. A total of 32 datasets across 23 sources, amounting to 1.9 terabytes of data were down sampled and processed to a 48.6 gigabytes general dataset in an HDF5 file format. We release this first compilation as Melt-Pool-Kinetics. This dataset aims to facilitate downstream analytics applications such as machine learning (ML) for in-situ monitoring, process optimization, and closed-loop feedback for AM quality prediction and process repeatability. The dataset is organized into structured levels, offering both raw and processed image data in the standardized HDF5 file format. Additionally, we provide a diverse subset for large datasets, which could enhance model training efficiency and boost performance on AM-related tasks. The current version can be extended in the future by adding latest datasets to enhance its applicability.