Informed integration of data-driven models for materials processing has yet to be fully realized due to data science knowledge gaps, incomplete materials and processing datasets, and a lack of data-driven tools designed explicitly for classically trained engineers. On the other hand, modern particle size distribution analyzers enable hundreds of thousands of particle-to-particle size, shape, and morphological properties to be easily gathered. Accordingly, we present suitable data analysis, sharing, and visualization approaches for developing a powder particle classification based upon powder morphology and size metrics for flowability on demand (FoD). We demonstrate the utility of Tableau Dashboards connected to a live powder database for making data-driven integration convenient to assess, visualize, and analyze particulate data, thus making comparisons between the features of individual powders and micro-particulate constituents accessible for traditional materials scientists and engineers. The FoD framework reduced the time taken for common workflows for FoD-based tasks.

This work was published and presented at the AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification: Poster Session of the TMS2022 Annual Meeting & Exhibition. Please find the paper here.