Control# 3387-1790 baylor university with pacific northwestern n vision open 2024 project title ''additive friction surfacing for large scale additive manufacturing with improved service life in…

The Department of Energy Advanced Research Projects Agency awarded Baylor University a $3.874 million Cooperative Agreement on January 15, 2026, under the Advanced Research Projects Agency - Energy program (CFDA 81.135) to develop an additive friction surfacing (AFS) manufacturing process for large-scale additive manufacturing of steel components.

The project will develop AFS processes to enable cost-competitive near net shape manufacturing of 10,000-pound steel components in less than 200 hours at half the cost of wire arc additive manufacturing. The project team will leverage process physics, residual stress simulation tools, advanced process controls, machine learning-assisted quality assurance methods, and machine design capabilities to create two 1,000-pound defect-free full-scale subsections of a Voith turbine using the developed AFS processes.

Work is performed in Waco, Texas, with performance extending through January 14, 2029. Baylor University is the prime recipient.

Brigham Young University and Advanced Manufacturing LLC serve as sub-recipients on the award. Both sub-awards were obligated on February 23, 2026, for additive friction surfacing work supporting large-scale additive manufacturing.

Control# 3387-1790 baylor university with pacific northwestern n vision open 2024 project title ''additive friction surfacing for large scale additive manufacturing with improved service life in extreme environments'' the purpose of this project is to develop a new additive friction surfacing (AFS) manufacturing process that will enable cost competitive near net shape (NNS) large scale additive manufacturing (lsam) of 10,000LBS steel components in less than 200 hours and at half the cost of wire ARC additive manufacturing (waam). This project intends to leverage process physics, world class residual stress simulation tools, advanced process controls, machine learning assisted quality assurance methods, and machine design capabilities of the project team to create two 1,000 LBS. Defect free full-scale subsection of a voith turbine using the developed AFS processes. New award