Title: Robust topology optimization accounting for spatially varying manufacturing errors
Authors: Schevenels, Mattias ×
Lazarov, B. S
Sigmund, O #
Issue Date: 2011
Publisher: North-Holland Pub. Co.
Series Title: Computer Methods in Applied Mechanics and Engineering vol:200 issue:49-52 pages:3613-3627
Abstract: This paper presents a robust approach for the design of macro-, micro-, or nano-structures by means of topology optimization, accounting for spatially varying manufacturing errors. The focus is on structures produced by milling or etching; in this case over- or under-etching may cause parts of the structure to become thinner or thicker than intended. This type of error is modeled by means of a projection technique: a density filter is applied, followed by a Heaviside projection, using a low projection threshold to simulate under-etching and a high projection threshold to simulate over-etching. In order to simulate the spatial variation of the manufacturing error, the projection threshold is represented by a (non-Gaussian) random field. The random field is obtained as a memoryless transformation of an underlying Gaussian field, which is discretized by means of an EOLE expansion. The robust optimization problem is formulated in a probabilistic way: the objective function is defined as a weighted sum of the mean value and the standard deviation of the structural performance. The optimization problem is solved by means of a Monte Carlo method: in each iteration of the optimization scheme, a Monte Carlo simulation is performed, considering 100 random realizations of the manufacturing error. A more thorough Monte Carlo simulation with 10000 realizations is performed to verify the results obtained for the final design. The proposed methodology is successfully applied to two test problems: the design of a compliant mechanism and a heat conduction problem. (C) 2011 Elsevier B.V. All rights reserved.
ISSN: 0045-7825
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Structural Mechanics Section
Research Group CAAD, Design- and Building Methodology (-)
× corresponding author
# (joint) last author

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