msamDB: Towards addressing data-scarcity challenges in L-PBF additive manufacturing

Abstract

Data science techniques, particularly machine learning (ML), have proven to be valuable tools in PBF-LM research. While ML can rapidly model the large process parameter space of PBF-LM, their efficacy is dependent on large, informative and diverse training datasets. However, scarcity in the development and availability of such datasets is an on-going challenge. This work outlines the on-going progress to address this challenge through the development of a database platform, tentatively named msamDB (multi-scale additive manufacturing database). This platform, specifically created to manage PBF-LM academic research data, is a modular, extensible and scalable database that can promote data-sharing among researchers. The initial architecture of msamDB focuses on surface roughness data generated throughout the PBF-LM lifecycle. This work highlights the findings and challenges encountered in the design, implementation and pilot data population stages of msamDB. In its current stage, msamDB data spans data from approximately 30 builds, multiple research and industry studies, 3 different powder materials and a broad range of process parameters. Data has been collected from various stages such as powder characterization, build planning, process parameter selection, surface characterization, etc. In reference to surface roughness measurements, the database currently has more than 1000 data points across various surface orientations. This work represents first known effort to curate research PBF-LM data at scale for PBF-LM. The potential impact of such a database is to promote federated data for PBF-LM researchers, which allows for data-driven model development to have increased usability.