Penn State researchers to create dynamic model for additive manufacturing of metal components

A two-year NSF grant will support research on modeling and advanced control for additive manufacturing of metal components.

Pennsylvania State University (Penn State; State College, PA) researchers have received a two-year, $277,000 grant from the National Science Foundation (NSF; Arlington, VA) that will support fundamental research on an integrated paradigm of modeling and advanced control for additive manufacturing of critical metal components.

Qian Wang, professor of mechanical engineering, is the principal investigator (PI) 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.

Wang and her research group are working in three areas—modeling, sensing and control—to streamline these laser additive manufacturing processes, particularly direct metal deposition.

Pan, whose area of expertise is finite element modeling, developed high-fidelity finite element analysis (FEA) software (formerly called CUBES and now sold to Autodesk), which provides a thermo-mechanical modeling capability for additive manufacturing processes, Wang says. Wang is working on a simplified 3D model that will capture the main features and characteristics of Pan's model, and yet be flexible enough for her to implement good control designs.

Reutzel's expertise, Wang says, is in sensing. His work 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.

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

The long-term goal of the team's work is to help reduce manufacturing costs and increase competitiveness of US industry by decreasing time required during the trial-and-error process and by improving the accuracy and stability of the additive manufacturing process. Their modeling and control design will be tested and evaluated at Penn State's Center for Innovative Materials Processing through Direct Digital Deposition (CIMP-3D).

For more information, please visit

More in Additive Manufacturing