Xiayun (Sharon) Zhao, assistant professor of mechanical engineering and materials science at Pitt, will lead the research. She'll work with Albert To, associate professor of mechanical engineering and materials science at Pitt, and Richard W. Neu, professor in the Georgia Institute of Technology's School of Mechanical Engineering.
The team will use machine learning to develop a cost-effective method for rapidly evaluating, either in-process or offline, the hot gas path turbine components (HGPTCs) that are created with LPBF additive manufacturing (AM) technology.
Zhao said HGPTCs have a tendency toward porous defects, which makes them more susceptible to overheating.
"LPBF 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,” she said. “It's critical to have a good quality assurance method before putting them to use. The quality assurance framework we are developing will immensely reduce the cost of testing and quality control and enhance confidence in adopting the LPBF process to fabricate demanding HGPTCs."