Pitt mechanical engineers receive $350,000 NSF grant to develop fast computational modeling for additive manufacturing

PITTSBURGH (August 16, 2016) … As additive manufacturing (AM), or 3D printing, becomes more commonplace, researchers and industry are seeking to mitigate the distortions and stresses inherent in fabricating these complex geometries. Researchers at the University of Pittsburgh’s Swanson School of Engineering and Pittsburgh-based manufacturer Aerotech, Inc. recently received a $350,000 grant from the National Science Foundation to address these design issues by developing new, fast computational methods for additive manufacturing.

The proposal, “Novel Computational Approaches to Address Key Design Optimization Issues for Metal Additive Manufacturing,” is a three-year, $350,000 GOALI (Grant Opportunities for Academic Liaison with Industry) grant funded by the NSF’s Division of Civil, Mechanical and Manufacturing Innovation (CMMI). The team, based in the Swanson School’s Department of Mechanical Engineering and Materials Science, includes Associate Professor and Principal Investigator Albert To; and co-PIs Assistant Professor Sangyeop Lee and Adjunct Associate Professor Stephen Ludwick. Aerotech, Inc. will partner with Pitt by providing designs and evaluation. The group’s research is an extension of previous funding from the Research for Advanced Manufacturing in Pennsylvania program (RAMP).

“The ability to create geometrically complex shapes through additive manufacturing is both a tremendous benefit and a significant challenge,” Dr. To said. “Optimizing the design to compensate for residual distortion, residual stress, and post-machining requirements can take days or even months for these parts.”

To mitigate these challenges, Dr. To and his group will first develop a simple yet accurate thermomechanics model to predict residual stress and distortion in an AM part. Next, they will develop a topology optimization method capable of generating designs with both free-form surfaces and machining-friendly surfaces. According to Dr. To, this will compensate for the geometric complexity and organic nature of AM parts, which contribute to their potential for distortion and post-machining problems. These approaches will then be developed and tested using real parts and design requirements provided by Aerotech. 

Aerotech’s Stephen Ludwick expects that "the tools developed through this collaboration will allow us to produce the complex parts enabled by additive manufacturing with a minimum of trial-and-error and rework. This in turn allows us to design stiff and lightweight components in our high-speed motion systems which are also used by other companies engaged in advanced manufacturing."

“By utilizing advanced mechanic theory, we hope to reduce design optimization of additive manufactured parts to minutes, thereby reducing the time of design life cycle,” Dr. To said. “This would lead to wider adoption of AM by the U.S. manufacturing base and further improve the economic sustainability of the additive manufacturing process.”

About the NSF GOALI Grant
Grant Opportunities for Academic Liaison with Industry (GOALI) promotes university-industry partnerships by making project funds or fellowships/traineeships available to support an eclectic mix of industry-university linkages. Special interest is focused on affording the opportunity for: 

• Faculty, postdoctoral fellows, and students to conduct research and gain experience in an industrial setting;
• Industrial scientists and engineers to bring industry perspectives and integrative skills to academe; and
• Interdisciplinary university-industry teams to conduct research projects. 

This solicitation targets high-risk/high-gain research with a focus on fundamental research, new approaches to solving generic problems, development of innovative collaborative industry-university educational programs, and direct transfer of new knowledge between academe and industry. GOALI seeks to fund transformative research that lies beyond that which industry would normally fund.

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Image above: The supporting structures failed for these four fatigue test bars. The stress buildup in the longer length of the bars created an excessive curling force on the outer edges of the support structures, resulting in fracture.

Image below: For larger internal lattice networks, if the open run of the lattice network is close to the maximum build span, the solid skinned top surface of the lattice network will risk an incomplete closure. Because of internal stresses generated during the build, these unconnected areas raise and in turn cause the recoating blade to strike them, which results in a failed build.