DREAM-TEAM: Developing a Robust Ecosystem for Additive Manufacturing of Tungsten for Extreme Applications and Management.

Dr. Sougata Roy, Iowa State University (Principal Investigator)

Dr. Yachao Wang, University of North Dakota (Co-Investigator)

Tungsten (W) has been a main candidate material for the first wall of fusion reactors due to its excellent high-temperature strength and melting temperature, resistance to erosion under high-energy neutron irradiation, and low tritium retention. Despite desirable mechanical properties, W suffers from poor ductility that inhibits the synthesis of W-based alloys via conventional manufacturing routes. Near-net shaping via additive manufacturing (AM) methods provide an opportunity to address these challenges, however, crack susceptivity and high-power density remain significant challenges even with AM of W-based components. Recently, the US Department of Energy has ramped up research activities related to exploring the potential of metal AM processes for fabrication of W alloy parts for fusion reactors, specifically for use in plasma-facing components (PFCs), breeder blankets, and thermal/radiation shields. If funded, the proposed ‘DREAM-TEAM’ project can play a crucial role in advancing the AM of W and its alloys through a comprehensive investigation combining physics-based modeling, machine learning, computational simulation of AM process, fabrication of samples via AM, material characterization, ion irradiation behavior and high-throughput mechanical testing. Such holistic contribution will be achieved through synergistic efforts from scientists and engineers with unique research expertise from two research universities (Iowa State University and University of North Dakota) and three national laboratories (Oak Ridge National Laboratory, Argonne National Laboratory, and Ames National Laboratory). The primary research objective of this proposed project is to demonstrate metal AM processing of W-based alloys using laser powder-blown directed-energy deposition (DED) as a major route to fabricate near-net shape test articles for extreme environment applications. Due to its inherent challenges with processing, exploring scientific details of manufacturing, microstructural characteristics, and mechanical properties, the experimental and computational (fluid dynamics-based simulation of melt pool) efforts will be complemented by unique theoretical capabilities of combining density functional theory (DFT) with Artificial Intelligence (AI) /Machine-learning (ML). This project will generate an avenue to establish research collaboration between two early career faculties from neighboring states (IA and ND) and form a ‘dream-team’ by working with three major national laboratories to work in the advanced manufacturing domain.