About P.R.D.C.T (Powder Rheometric Device Characterizing Torque)

The team is developing a test apparatus designed to measure agitation torque for a variety of powdered materials and agitator geometries. The data collected from this system will be used to create an empirical model that can accurately predict motor sizing requirements for feeding applications. By improving the understanding of how material properties, material quantity, and agitator design influence torque demands, this project aims to provide a more reliable and data-driven approach to motor selection.

Problem Statement

Built Protype Design

Motor sizing is a critical aspect of feeder design. Undersized motors can lead to feeder failure and unreliable operation, while oversized motors increase costs and may contribute to unnecessary wear and component damage. Because the required motor torque is directly affected by the interaction between the material being handled and the agitator geometry, selecting the appropriate motor is often a challenging process.

Currently, the relationship between feeder materials, agitator designs, and the resulting torque requirements is not well understood. As a result, many companies in the feeding industry rely heavily on past experience and trial-and-error when selecting motors. This approach can be time-consuming, costly, and inconsistent across different applications.

Sponsors & Advisors

Professor

Yvonne Traynham

Project Mentor

Professor

Twan Capehart

Project Mentor

Professor

Mauricio Chagas

Project Mentor

Associate Dean, FSU-PC

Irvin Clark, EdD

Project Sponsor

Industry

Merrick Industries & Ed Boardway

Project Sponsor

Randy Hanna, EdD

Project Sponsor

Dean, FSU-PC

Project Sponsor

Sandy & Jim Dafoe

Gallery

The test apparatus provides a controlled environment for collecting accurate torque data across a range of operating conditions. Through systematic testing, the device generates valuable insights into the factors that influence agitation torque. These results support the development of predictive models that enable engineers to size motors more accurately and efficiently.

Documents