NSF grant to create dynamic model for advanced manufacturing of metal components

06/28/2016

UNIVERSITY PARK, Pa. – Penn State researchers have received a two-year, $277,000 grant from the National Science Foundation (NSF) that will support fundamental research on an integrated paradigm of modeling and advanced control for additive manufacturing (AM) of critical metal components.

Qian Wang, professor of mechanical engineering, is the principal investigator of the project, titled “Modeling and Control for Laser Based Additive Manufacturing Processes.”

She is collaborating with co-PIs Ted Reutzel, head of the laser process technology department at Penn State’s Applied Research Laboratory and an affiliate faculty member in the Department of Engineering Science and Mechanics, and Pan Michaleris, a former professor in the Department of Mechanical and Nuclear Engineering.

“Additive manufacturing is currently very popular with the research community but it hasn’t been widely adopted by industry due to challenges in the process, such as accuracy of part geometry and mechanical and material properties of the processed part,” said Wang.

She said without a good understanding of some of these challenges, people often now experiment with additive manufacturing by trial and error.

“For instance, someone may want to build something but they might only have a rough estimate of what the laser power and laser scanning speeds should be in order to get the results they want to achieve. If it doesn’t work they have to change those parameters and redo it. This can be costly in terms of time and money,” explained Wang.

Wang and her research group are working in three areas—modeling, sensing and control—in order to streamline these laser-based AM processes, in particular direct metal deposition.

“Pan’s area of expertise is finite element modeling. He developed high-fidelity finite element analysis, or FEA, software (formerly called CUBES and now sold to Autodesk), which provides a thermo-mechanical modeling capability for AM processes. I am working on a simplified 3D model that will capture the main features and characteristics of Pan’s model and yet be flexible enough for me to implement good control designs,” said Wang.

Reutzel’s expertise, said Wang, is in sensing.

“Ted provides real-time sensing and measurements such as temperature and geometry of a part in build, and such information will be used in the implementation of a feedback control,” she said.

Wang said one key difference between her proposed 3D model and existing 1D models is that hers will account for these geometric effects and thermal history.

“Current control-oriented models tend to ignore thermal history, which can affect the microstructure, residual stress and distortion of the final product,” she explained.

Once Wang’s model is developed, users can provide her with a specific target geometry and she, in turn, will be able to design the process parameters in order to achieve the desired piece.

The long-term goal of the trio’s work is to help reduce manufacturing costs and increase competitiveness of U.S. industry by decreasing time required during the trial-and-error process and by improving the accuracy and stability of the AM process.

The researchers’ modeling and control design will be tested and evaluated at Penn State’s Center for Innovative Materials Processing through Direct Digital Deposition.

The project is funded through the NSF Civil, Mechanical and Manufacturing Innovation Dynamics, Control and System Diagnostics Program.

 

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MEDIA CONTACT:

Stefanie Tomlinson

stomlinson@engr.psu.edu

Qian Wang

Qian Wang is serving as the principal investigator for an NSF-funded project on modeling and advanced control for additive manufacturing.

 
 

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