Journal ArticleDOI
Non-probabilistic reliability-based topology optimization of geometrically nonlinear structures using convex models
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TLDR
In this paper, a non-probabilistic reliability-based topology optimization method for the design of continuum structures undergoing large deformation is presented. But the authors do not consider the nonlinearity of the structural system.About:
This article is published in Computer Methods in Applied Mechanics and Engineering.The article was published on 2009-09-01. It has received 186 citations till now. The article focuses on the topics: Probabilistic-based design optimization & Nonlinear programming.read more
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A survey of structural and multidisciplinary continuum topology optimization: post 2000
TL;DR: Topology optimization is the process of determining the optimal layout of material and connectivity inside a design domain this paper, which is the same as the problem of finding the optimal configuration of a set of components.
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Current and future trends in topology optimization for additive manufacturing
Jikai Liu,Andrew T. Gaynor,Shikui Chen,Zhan Kang,Krishnan Suresh,Akihiro Takezawa,Lei Li,Junji Kato,Jinyuan Tang,Charlie C. L. Wang,Lin Cheng,Xuan Liang,Albert C. To +12 more
TL;DR: The motivation of this perspective paper is to summarize the state-of-art topology optimization methods for a variety of AM topics and the hope is to inspire both researchers and engineers to meet the challenges with innovative solutions.
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Interval uncertain method for multibody mechanical systems using Chebyshev inclusion functions
TL;DR: In this article, a Chebyshev inclusion function based on the truncated Chebyhev series is proposed to achieve sharper and tighter bounds for meaningful solutions of interval functions, to effectively handle the overestimation caused by the wrapping effect, intrinsic to interval computations.
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Recent development in structural design and optimization
Xu Guo,Gengdong Cheng +1 more
TL;DR: A brief description of the current status of structural optimization by reviewing some significant progress made in the last decades is presented.
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Introducing Loading Uncertainty in Topology Optimization
TL;DR: This paper introduces an efficient and accurate approach to robust structural topology optimization to minimize expected compliance with uncertainty in loading magnitude and applied direction where uncertainties are assumed normally distributed and statistically independent.
References
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Generating optimal topologies in structural design using a homogenization method
Martin P. Bendsøe,Noboru Kikuchi +1 more
TL;DR: In this article, the authors present a methodology for optimal shape design based on homogenization, which is related to modern production techniques and consists of computing the optimal distribution in space of an anisotropic material that is constructed by introducing an infimum of periodically distributed small holes in a given homogeneous, i.i.
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The method of moving asymptotes—a new method for structural optimization
TL;DR: In this article, a new method for non-linear programming in general and structural optimization in particular is presented, in which a strictly convex approximating subproblem is generated and solved.
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Optimal shape design as a material distribution problem
TL;DR: In this article, various ways of removing this discrete nature of the problem by the introduction of a density function that is a continuous design variable are described. But none of these methods can be used for shape optimization in a general setting.
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A level set method for structural topology optimization
TL;DR: A new approach to structural topology optimization that represents the structural boundary by a level set model that is embedded in a scalar function of a higher dimension that demonstrates outstanding flexibility of handling topological changes, fidelity of boundary representation and degree of automation.
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Structural optimization using sensitivity analysis and a level-set method
TL;DR: A new numerical method based on a combination of the classical shape derivative and of the level-set method for front propagation, which can easily handle topology changes and is strongly dependent on the initial guess.