Journal ArticleDOI
Robust topology optimization under material and loading uncertainties using an evolutionary structural extended finite element method
TLDR
This paper is among the first to use the XFEM in studying the robust topology optimization under uncertainty and there is no need for any post-processing techniques, so the effectiveness of this method is justified by the clear and smooth boundaries obtained.Abstract:
This research presents a novel algorithm for robust topology optimization of continuous structures under material and loading uncertainties by combining an evolutionary structural optimization (ESO) method with an extended finite element method (XFEM). Conventional topology optimization approaches (e.g. ESO) often require additional post-processing to generate a manufacturable topology with smooth boundaries. By adopting the XFEM for boundary representation in the finite element (FE) framework, the proposed method eliminates this time-consuming post-processing stage and produces more accurate evaluation of the elements along the design boundary for ESO-based topology optimization methods. A truncated Gaussian random field (without negative values) using a memory-less translation process is utilized for the random uncertainty analysis of the material property and load angle distribution. The superiority of the proposed method over Monte Carlo, solid isotropic material with penalization (SIMP) and polynomial chaos expansion (PCE) using classical finite element method (FEM) is demonstrated via two practical examples with compliances in material uncertainty and loading uncertainty improved by approximately 11% and 10%, respectively. The novelty of the present method lies in the following two aspects: (1) this paper is among the first to use the XFEM in studying the robust topology optimization under uncertainty; (2) due to the adopted XFEM for boundary elements in the FE framework, there is no need for any post-processing techniques. The effectiveness of this method is justified by the clear and smooth boundaries obtained.read more
Citations
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Introducing Loading Uncertainty in Topology Optimization
TL;DR: In this article, an efficient and accurate approach to robust structural topology optimization is proposed to minimize expected compliance with uncertainty in loading magnitude and applied direction, where uncertainties are assumed normally distributed and statistically independent.
Journal ArticleDOI
A feature-driven robust topology optimization strategy considering movable non-design domain and complex uncertainty
Zeshang Li,Lei Wang,Zhenxian Luo +2 more
TL;DR: Wang et al. as mentioned in this paper proposed a feature-driven robust topology optimization strategy considering movable non-design domain and complex uncertainty, and the sensitivity of the robust optimization model is derived based on the shape derivative principle, which provides the basis for the implementation of gradient based optimization algorithm.
Journal ArticleDOI
Topology Optimization for Design of Hybrid Lattice Structures with Multiple Microstructure Configurations
Journal ArticleDOI
Proportional Topology Optimization under Reliability-based Constraints
TL;DR: The Proportional Topology Optimization method renders the possibility of treating the stress constraints in a unified way, which allows topologies that at the same time preserve structural reliability and optimize costs.
References
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Journal ArticleDOI
Robust Topology Optimization Under Load and Geometry Uncertainties by Using New Sparse Grid Collocation Method
TL;DR: In this paper, the adaptive sparse grid collocation (ASGC) method combined with the uncertainty models provides a computationally cheap alternative to previously introduced stochastic optimization methods based on Monte Carlo sampling.
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