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Journal article · Preprint article

Three‐dimensional manufacturing tolerant topology optimization with hundreds of millions of local stress constraints

From

University of São Paulo1

Department of Mechanical Engineering, Technical University of Denmark2

Acoustic Technology, Department of Electrical Engineering, Technical University of Denmark3

Solid Mechanics, Department of Mechanical Engineering, Technical University of Denmark4

In topology optimization, the treatment of stress constraints for very large scale problems (more than 100 million elements and more than 600 million stress constraints) has so far not been tractable due to the failure of robust agglomeration methods, i.e. their inability to accurately handle the locality of the stress constraints.

This paper presents a three‐dimensional design methodology that alleviates this shortcoming using both deterministic and robust problem formulations. The robust formulation, based on the three‐field density projection approach, is extended and proved necessary to handle manufacturing uncertainty in three‐dimensional stress‐constrained problems.

Several numerical examples are solved and further post‐processed with body‐fitted meshes using commercial software. The numerical investigations demonstrate that: (1) the employed solution approach based on the augmented Lagrangian method is able to handle very large problems, with hundreds of millions of stress constraints; (2) three‐dimensional stress‐based results are extremely sensitive to slight manufacturing variations; (3) if appropriate interpolation parameters are adopted, voxel‐based (fixed grid) models can be used to compute von Mises stresses with excellent accuracy; and (4) in order to ensure manufacturing tolerance in threedimensional stress‐constrained topology optimization, a combination of double filtering and more than three density field realizations may be required.

Language: English
Publisher: John Wiley & Sons, Inc.
Year: 2021
Pages: 548-578
ISSN: 10970207 and 00295981
Types: Journal article and Preprint article
DOI: 10.1002/nme.6548
ORCIDs: 0000-0002-0684-9262 , Aage, Niels , 0000-0003-4127-5337 and Sigmund, Ole

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