University Of Pittsburgh Awarded Over $1 Million To Develop Quality Assurance For 3D Printed Turbine Components

- Jul 17, 2019-

Researchers from the Swanson School of Engineering have received over $1 million in combined funding from the U.S. Department of Energy (DoE) and the University of Pittsburgh. The funding is intended to support the development of an effective quality assurance method for the additive manufacturing of new-generation gas turbine components. Lasting three years, Xiayun (Sharon) Zhao, PhD, assistant professor of mechanical engineering and materials science at Pitt, will lead the project.  

The DoE, through its University Turbine Systems Research program, awarded a sum of $802,400 to the researchers, and Pitt provided an additional $200,600 (bringing the total grant to $1,003,000). Furthermore, ANSYS, an engineering simulation software company, will serve as an industrial partner in the project, providing further support. 

Developing quality assurance for laser powder bed fusion technology

Alongside Dr. Zhao, the research team comprises of 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. 

Using the allocated funding, the research team are looking to establish a cost-effective method of quality assurance for hot gas path turbine components (HGPTCs), that are specifically created with laser powder bed fusion (LPBF) technology. Dr. Zhao explains that the efficient properties of LPBF is an alluring option for developing HGPTCs: “LPBF AM is capable of making complex metal components with reduced cost of material and time.” 

However, there are possible defects that are present within LPBF additive manufactured components that could prove detrimental when implemented within HGPTCs. Therefore, developing the new quality assurance method is a necessary step for the production of next-generation HGPTCs using additive manufacturing. The team will make use of machine learning in order to rapidly evaluate HGPTCs produced using LPBF additive manufacturing.

“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,” adds Dr. Zhao.


Xiayun Zhao, PhD, assistant professor of mechanical engineering and materials science, (left) and Albert To, PhD, associate professor of mechanical engineering and materials science. Photo via University of Pittsburgh.

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