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Gu Research Group - UC Berkeley
Gu Research Group - UC Berkeley
March 2024 - present
March 2024 - present
In collaboration with Brennan Birn and Kyle J. Woody
In collaboration with Brennan Birn and Kyle J. Woody
Introduction
Introduction
Lattice structures exhibit high strength and energy absorption properties. Relative to their density, they are mechanically superior to solid materials and are ideal in lightweighting applications. Here, we apply a generative design algorithm to generate microstructures with optimized mechanical properties.
Methods
Methods
We employed Autodesk Fusion 360's Generative Design function, an automated design tool that can build an optimized solution without a starting shape or input mesh. We analyzed and optimized three strut-based lattice structures at various relative densities: body-centered cubic (BCC), simple cubic (SC), and a structure inspired by the Victoria Water Lily (Ashok et. al.).
The performance of the lattice structures outputted from the generative design algorithm was evaluated for high specific strength, stiffness, and energy absorption through FEA, homogenizations, and experimental testing.
During this project, I was responsible for:
Modeling unoptimized lattices and generating optimized structures through generative design in Autodesk Fusion 360
Fabricating all testing specimens using SLA 3D printing
Experimental quasi-static compression testing and data analysis
Finite element analysis in ABAQUS and homogenizations in nTop
Conclusions
Conclusions
Our results show that generative design can greatly increase effective stiffness, strength, and isotropy of each microstructure relative to their conventional geometry. Generative design can tune the stiffness matrix of common stretching and bending-dominated lattice structures, as well as bioinspired lattice structures, while decreasing the anisotropy of each lattice structure relative to the desired loading axis.
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