Thermal-mechanical Predictive Algorithm for Electrically-assisted Manufacturing Processes


There is a current need for improved lightweight materials for the automotive and aerospace industries to improve fuel economy, material durability and strength, and improve performance. These needs can be addressed using lightweight metals such as aluminum and titanium and by reducing the amount of materials by using stronger materials in place of weaker materials. Unfortunately, the manufacturability is limited by the strength and formability of these materials and can be difficult to achieve using traditional manufacturing methods. Electrically-Assisted Manufacturing (EAM) is an upcoming manufacturing method in which electricity is applied to a part undergoing deformation in processes such as: bulk deformation processes (forging, rolling, extrusion), sheet metal processes (stretch forming, deep drawing), and joining processes (friction welding). However, EAM has not been able to be modeled and predicted, therefore only allowing implementation through a trial and error method, therefore limiting industry adoption.

This technology accurately predicts both the forming loads and thermal characteristics of a metal work piece as it is being deformed using the new Electrically-Assisted Manufacturing (EAM) technique and provides a methodology for the control of a manufacturing process that applies a direct electrical current through a metallic work piece concurrently with the mechanical process in order to modify the material flow characteristic. The EAM technique has been proven experimentally, but it is unable to be used in industry because there is currently no way to predict force/thermal aspects without costly and time-consuming trial-and-error processes. This provides an analytical model for prediction of EAM processes such as forging, bending, stretch forming, and any other applicable processes.


·         Automotive and Aerospace Materials

·         Other industries benefitting from lightweight materials



·         Improved prediction of EAM processes

·         Increased likelihood of industry adoption

·         Increased manufacturability


Inventors:                           Cristina Bunget, Wesley Salandro, Laine Mears

Patent Status:                    A patent application has been filed

Licensing Status:               This technology is available for licensing

CURF Reference:               2011-067 and 2011-082


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Patent Information:
Advanced Materials
For Information, Contact:
Chris Gesswein
Director of Licensing
Clemson University Research Foundation
Cristina Bunget
Wesley Salandro
Laine Mears
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