Topology optimization (TO) is a generative design approach that is used to create new structures that are optimal for their purpose. Standard parameterizations of the TOproblem 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 parametrization of the TO problem uses geometry projection of known-manufacturable building blocksinto the design domain, but the existing library of building blocks compatible with projection is small. This project sought to expand this library with new shapes, and to determine whether theaddition of these new shapes could be used to design unit cells for micro-architected materials (MAMs) with improved stiffness-to-weight ratio compared with traditional designs,while retaining compatibility with additive manufacturing processes.Due to a reduction in scope, effort was focused on truss lattices with hollow, tapered, and curved struts. Hollowtruss lattices were found that outperform traditional designs like the octet truss in both stiffness and isotropy, as well as providing a decoupling between the stiffness and relative density. These new designs are expected to improve the performance of functionally graded structures in programmatic applications.
Luo, Yunfeng, et al. "A projection-based method for topology optimization of structures with graded surfaces." International Journal for Numerical Methods in Engineering, vol. 118, no. 11, Feb. 2019. https://doi.org/10.1002/nme.6031
Luo, Yunfeng, Li, Quhao, & Liu, Shutian (2019). A projection-based method for topology optimization of structures with graded surfaces. International Journal for Numerical Methods in Engineering, 118(11). https://doi.org/10.1002/nme.6031
Luo, Yunfeng, Li, Quhao, and Liu, Shutian, "A projection-based method for topology optimization of structures with graded surfaces," International Journal for Numerical Methods in Engineering 118, no. 11 (2019), https://doi.org/10.1002/nme.6031
@article{osti_1650430,
author = {Luo, Yunfeng and Li, Quhao and Liu, Shutian},
title = {A projection-based method for topology optimization of structures with graded surfaces},
annote = {Topology optimization (TO) is a generative design approach that is used to create new structures that are optimal for their purpose. Standard parameterizations of the TOproblem 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 parametrization of the TO problem uses geometry projection of known-manufacturable building blocksinto the design domain, but the existing library of building blocks compatible with projection is small. This project sought to expand this library with new shapes, and to determine whether theaddition of these new shapes could be used to design unit cells for micro-architected materials (MAMs) with improved stiffness-to-weight ratio compared with traditional designs,while retaining compatibility with additive manufacturing processes.Due to a reduction in scope, effort was focused on truss lattices with hollow, tapered, and curved struts. Hollowtruss lattices were found that outperform traditional designs like the octet truss in both stiffness and isotropy, as well as providing a decoupling between the stiffness and relative density. These new designs are expected to improve the performance of functionally graded structures in programmatic applications.},
doi = {10.1002/nme.6031},
url = {https://www.osti.gov/biblio/1650430},
journal = {International Journal for Numerical Methods in Engineering},
issn = {ISSN 0029-5981},
number = {11},
volume = {118},
place = {United States},
publisher = {Wiley},
year = {2019},
month = {02}}
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1650430
Report Number(s):
LLNL-JRNL--795634; 997039
Journal Information:
International Journal for Numerical Methods in Engineering, Journal Name: International Journal for Numerical Methods in Engineering Journal Issue: 11 Vol. 118; ISSN 0029-5981