Researchers at the University of Pittsburgh’s Swanson School of Engineering and Pittsburgh-based manufacturer Aerotech Inc have received a US$350,000 grant from the National Science Foundation to develop new, fast computational methods for additive manufacturing (AM).
The proposal, entitled ‘Novel Computational Approaches to Address Key Design Optimization Issues for Metal Additive Manufacturing,’ is a three-year, 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.
‘The ability to create geometrically complex shapes through AM 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 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 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.
Lightweight components
‘The tools developed through this collaboration will allow us to produce the complex parts enabled by AM with a minimum of trial-and-error and rework,’ said Aerotech’s Stephen Ludwick. ‘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 AM parts to minutes, thereby reducing the time of design life cycle,’ Dr To added. ‘This would lead to wider adoption of AM by the US manufacturing base and further improve the economic sustainability of the AM process.’
This story is reprinted from material from the University of Pittsburgh, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier.
