Multiple-Instance Regression for Metal Powder Hall Flow Rate Prediction Using Augmented Particle Size and Shape Data

Abstract

This study investigates the relationship between metallic powders and their flowability behavior (captured in terms of Hall flow rates using Hall flowmeters). Due to the many trait dependencies of powder flowability, which have made the formulation of a physical and mechanistic generalizable model difficult to resolve, this study seeks to develop an alternative data-driven framework based on powder size and shape characteristics for Hall-flow-rate predictions. A multiple-instance regression framework was both developed for processing multiple-instance powder data and compared with standard machine learning models. Data augmentation was found to improve the overall performance of the framework, although the limited dataset was a constraint. Still, the study contributes to ongoing efforts to identify traditional, associative, and generalizable patterns between powder properties and resultant flowability behaviors. The findings show promise for real-world applications with a larger dataset, such that this initial application of multiple instance regression frameworks for metal powder Hall-flow-rate predictions as a function of powder particle size and shape data can be scrutinized in full.