Robust topology optimization of multi-material lattice structures under material and load uncertainties

Enabled by advancements in multi-material additive manufacturing, lightweight lattice structures consisting of networks of periodic unit cells have gained popularity due to their extraordinary performance and wide array of functions. This work proposes a density-based robust topology optimization me...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers of Mechanical Engineering 2019-06, Vol.14 (2), p.141-152
Main Authors: CHAN, Yu-Chin, SHINTANI, Kohei, CHEN, Wei
Format: Article
Language:English
Subjects:
Engineering
lattice structures
load uncertainty
material uncertainty
Mechanical Engineering
multi-material
Research Article
robust topology optimization
Structural Topology Optimization
univariate dimension reduction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Enabled by advancements in multi-material additive manufacturing, lightweight lattice structures consisting of networks of periodic unit cells have gained popularity due to their extraordinary performance and wide array of functions. This work proposes a density-based robust topology optimization method for meso- or macro-scale multi-material lattice structures under any combination of material and load uncertainties. The method utilizes a new generalized material interpolation scheme for an arbitrary number of materials, and employs univariate dimension reduction and Gauss-type quadrature to quantify and propagate uncertainty. By formulating the objective function as a weighted sum of the mean and standard deviation of compliance, the tradeoff between optimality and robustness can be studied and controlled. Examples of a cantilever beam lattice structure under various material and load uncertainty cases exhibit the efficiency and flexibility of the approach. The accuracy of univariate dimension reduction is validated by comparing the results to the Monte Carlo approach.
ISSN:2095-0233
2095-0241
DOI:10.1007/s11465-019-0531-4