Scalable Design for Manufacturing, Modeling Optimization for Additive Manufacturing
Navy SBIR FY2018.1


Sol No.: Navy SBIR FY2018.1
Topic No.: N181-094
Topic Title: Scalable Design for Manufacturing, Modeling Optimization for Additive Manufacturing
Proposal No.: N181-094-0023
Firm: Corvid Technologies, LLC
145 Overhill Drive
Mooresville, North Carolina 28117
Contact: Aaron Ward
Phone: (704) 799-6944
Web Site: http://www.corvidtec.com
Abstract: The Navy seeks a framework for generating scalable lattice structures which can be utilized for developing new structural components that are lighter yet achieve improved structural performance. CorvidÉ?Ts design approach will utilize topology optimization to generate an initial distribution of material in the component design. Using the material distribution obtained for the topology optimized component, lattice with same corresponding mechanical properties will be seeded into the computational domain. In order to bridge the multiple scales of the lattice structures, a second round of topology optimization will be performed only at the boundary of the lattice unit cells. This will yield a continuous internal structure utilizing multiple scales of lattice structures to achieve both structural and mass efficiency.
Benefits: The envision tool developed under this effort can be adapted and applied to range of engineering Navy programs. There are several ongoing Navy programs looking to leverage additive manufacturing which could benefit from the design tool being developed. Programs such as MV-22B osprey and developing additive manufactured submersibles are example of current of previous programs which could directly benefit from the improvements developed under this SBIR effort. Outside the expansion into other Navy programs, this tool would be applicable to any engineering technology which weight and strength of paramount importance. The utility of the tool increases as AM becomes more integrated into commercial supply chains. This process is already underway where aerospace is utilizing AM to design engine components and in the automotive industry to design.

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