|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||M-10: Investigating the Suitability of Tableau Dashboards and Decision Trees for Particulate Materials Science and Engineering Data Analysis
||Bryer Sousa, Richard Valente, Aaron Krueger, Eric Schmid, Danielle Cote, Rodica Neamtu
|On-Site Speaker (Planned)
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 by 81.13% for FoD-based tasks.
||Machine Learning, Powder Materials, Characterization