Topology optimization (TO) is a generative design approach used to create new structures optimal for their purpose. Standard parameterizations of the TO problem allow exploration of an arbitrarily complex design space but must be constrained to ensure manufacturability. These constraints tend to be computationally expensive and intrusive to the design process. A relatively new parameterization of the TO problem uses geometry projection of known manufacturable building blocks into the design domain. However, the existing library of building blocks compatible with projection is small.
Our project aimed to expand the building block library with new shapes. We also sought to determine whether the addition of new shapes could be used to design unit cells for micro-architected materials with improved stiffness-to-weight ratio compared with traditional designs, while retaining compatibility with additive manufacturing processes. Our effort focused on truss lattices with hollow, tapered, and curved struts. Hollow truss lattices were found that outperform traditional designs, such as the octet truss, in both stiffness and isotropy and provide decoupling between the stiffness and relative density. These new designs are expected to improve the performance of functionally-graded structures in programmatic applications.
Our research leveraged and enhanced Lawrence Livermore National Laboratory's core competencies in advanced materials and manufacturing as well as high-performance computing, simulation, and data science.
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