The U.S. Department of Energy, through its University Turbine Systems Research programme, has awarded $802,400 to researchers at the University of Pittsburgh’s Swanson School of Engineering, Pittsburgh, Pennsylvania, USA, to support research on an effective quality assurance method for the metal Additive Manufacturing of new-generation gas turbine components.
The three-year project received additional funding of $200,600 from the University of Pittsburgh, resulting in a total grant amount of $1,003,000.
Xiayun (Sharon) Zhao, PhD, Assistant Professor of Mechanical Engineering and Materials Science at Pittsburgh, will lead the research, working with Albert To, Associate Professor of Mechanical Engineering and Materials Science at Pittsburgh, and Richard W Neu, a Professor in the Georgia Institute of Technology’s School of Mechanical Engineering, Georgia, USA.
The team will reportedly use machine learning to develop a cost-effective method for rapidly evaluating, either in-process or offline, hot gas path turbine components (HGPTCs) created with Laser Powder Bed Fusion (L-PBF) Additive Manufacturing technology. Dr Zhao explained, “L-PBF AM is capable of making complex metal components with reduced cost of material and time. There is a desire to employ the appealing AM technology to fabricate sophisticated HGPTCs that can withstand higher working temperature for next-generation turbines.”
“However, because there’s a possibility that the components will have porous defects and be prone to detrimental thermomechanical fatigue, it’s critical to have a good quality assurance method before putting them to use,” she continued. “The quality assurance framework we are developing will immensely reduce the cost of testing and quality control and enhance confidence in adopting the L-PBF process to fabricate demanding HGPTCs.”
Ansys, an engineering simulation and design software developer based in Canonsburg, Pennsylvania, will serve as an industrial partner on the project.