GOALI: Novel Computational Approaches to Address Key Design Optimization Issues for Metal Additive Manufacturing

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

The additive manufacturing (AM), also called 3D printing, process is now capable of manufacturing complex-shaped metal components strong enough for structural applications. The capability of AM to fabricate complex geometries removes many conventional manufacturing constraints and largely expands the design space. However, there are key current issues that are impeding the marriage between AM and topology optimization: 1) Difficulty in machining topology optimized AM components due to their highly complex shapes, and 2) Inability to optimize AM part design for residual stress and distortion. This Grant Opportunity for Academic Liaison with Industry (GOALI) research project aims at establishing a robust topology optimization technology capable of accounting for residual distortion, residual stress, and post-machining requirements for additive manufactured (AM) components. The technology developed will significantly shorten the design phase during new AM product development, which will potentially lead to wider adoption of AM by the manufacturing base in the US. The skills and knowledge the students gain through this project will prepare them for active roles in securing U.S. leadership in engineering design and advanced manufacturing for future generations. The goal of this GOALI project is to address the aforementioned critical design issues by proposing and investigating two novel approaches. The first approach is an ultra-fast computational method based on the inherent strain theory for predicting residual stress and distortion in an AM part. The second approach is a level set feature-based topology optimization method capable of generating designs with both freeform surfaces and machining-friendly surfaces. These approaches will be developed and tested using real parts and design requirements provided by our industrial partner Aerotech. Utilizing this approach, a robust topology optimization method will be developed to optimize AM designs for residual stress, residual distortion, and post-machining requirements.