Metal powders are used as feedstock in a variety of manufacturing methods, wherein the metallic particulates may be deposited upon a substrate, spread upon a build plate, or melted layer-by-layer. Regardless of how the powder is consumed during a given manufacturing process, the ease with which a powder flows maintains a significant impact on the properties and behavior of the resultant component procured. This is especially true for cold spray additive manufacturing, which is a solid-state metallic particulate deposition process that requires suitable flowability to prevent component waviness as well as prevent nozzle clogging.

Particle-level geometrical properties, such as size and shape features, greatly impact the capacity of a powder to flow, or its flowability. Existing techniques for measuring a particle's flowability require manual powder processing which can be both time-consuming and vulnerable to man-made error. Both of these setbacks can be addressed through automatic, data-driven techniques trained to classify flowability from particle property measurements and expertly procured powder Hall Flow rates for training and testing.

To our knowledge, we formulated the first computational model for classifying flowability (when captured in terms of the industrially standardized Hall Flow rates) of a metal powder given its particle-level physical property measurements. After obtaining twenty-one metal powder samples from manufacturers, particle-level physical property measurements of each powder were measured.

We used these particle measurements to train a Decision Tree model to classify whether a particle's Hall Flow rate was fast or slow. With an achieved accuracy 98.04%, our work illustrates that powder flowability, through the lens of Hall Flow rates, can be accurately classified using data-driven techniques. This work lays a promising foundation for further exploration into predictive models for this cutting-edge domain.

Research concerned with the identification as well as quantification of satellites found within atomized metallic powders has recently demonstrated the promise of implementing Mark R-CNN’s, instance segmentation, and transfer learning to such ends.

Though the original research and development of such an approach demonstrated the functionality of the data-driven image analysis framework, questions remained in regards to the scale-ability of the Mask R-CNN-based model.

The present work demonstrates the fact that the originally formulated model can be expanded to include scanning electron micro-graphs to various powder types at variate magnifications (rather than the original case of micro-graphs of a single powder type at a single magnification).

This establishes a process that enables users to specifically target which images will have most impact on increasing generalize-ability and performance in order to optimize maximum improvement of the model with the least amount of images annotated. Beyond this, we also outline a method of auto-labeling satellites in images by using a trained model to increase its own training set size.