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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI-Enabled Materials Processing: Integrating Accelerated Experimental Workflows and Processing-Aware Machine Learning
Presentation Title A Programmable Powder Doser with 15+ Reservoirs and Automated Auger Swapping for AI-Enabled Alloy-Development Workflows
Author(s) Sam Charles, William Mulberry, Luke Winters, Sterling Baird
On-Site Speaker (Planned) Sam Charles
Abstract Scope AI-enabled processing workflows are only as fast as their slowest manual step—for alloy development, often feedstock metering and blending. We present an open-source programmable powder doser—the feedstock-preparation node of a closed-loop, Bayesian-optimization alloy-development pipeline—with a sub-$1,000 bill-of-materials target, far below commercial dispensers. Optimization-selected recipes are executed automatically, and dosed powders will feed ultrasonic atomization and laser powder bed fusion (L-PBF). A stepper-driven Archimedean auger with multi-mode agitation and tilt control accommodates cohesive, static-prone alloy-precursor powders—elemental or master-alloy form—dosed under inert atmosphere. Design targets include 15+ independently addressable reservoirs, 250 mL blends, and ±1 mg per-powder accuracy. Cross-contamination between recipes is prevented by design: dedicated per-powder augers exchanged by an automated auger-swap system. A machine-learning calibration layer, trained on gravimetric load-cell feedback, maps actuator parameters to dispensed mass per powder. We present the dosing mechanism, auger-swap system, calibration approach, and full open-source hardware and software package.
Proceedings Inclusion? Planned:
Keywords Process Technology, Powder Materials, Composites

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