Level set based robust shape and topology optimization under random field uncertainties

By Shikui Chen, Sanghoon Lee. Design Automation Conference, 2010

283 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Geology, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/43ae0c78966284756457aab152da64aa69b09246

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Ole Sigmund
2. Ramana V. Grandhi
3. Joshua D. Deaton
4. Zhan Kang
5. Mehdi Jalalpour
6. Mattias Schevenels
7. Geert Lombaert
8. Boyan S. Lazarov

In Geology

1. Tam H. Nguyen
2. Miche Jansen
3. Wei Chen
4. Geert Lombaert
5. Xu Guo
6. Chun Jie Wang
7. Li Zhang
8. Ramana V. Grandhi

In Industrial and Manufacturing Engineering

1. Ole Sigmund
2. Tam H. Nguyen
3. Miche Jansen
4. Lei Li
5. Krishnan Suresh
6. Wei Chen
7. Geert Lombaert
8. Xu Guo

Citations in the corpus

• Robust topology optimization accounting for spatially varying manufacturing errors (2011, methodology): “[9] also use the level set method for the design of robust structures with minimum compliance and for the design of robust compliant mechanisms.”
• Robust topology optimization of structures with imperfect geometry based on geometric nonlinear analysis (2015, methodology): “For example, numerical quadrature methods [7, 47] or simulation methods (e.”
• Doing Topology Optimization Explicitly and Geometrically—A New Moving Morphable Components Based Framework (2014, result): “Another advantage is that since the geometry of the structural components are described explicitly in the present computational framework, possible uncertainties of the components shapes usually arising from manufacture errors can also be dealt with in a relatively direct way compared to existing ap”
• Length scale and manufacturability in density-based topology optimization (2016, unknown): “Similar models for level set approaches have been proposed in [30] and [31].”
• Current and future trends in topology optimization for additive manufacturing (2018, methodology): “[236] employed the Karhunen-Loeve expansion of random fields to incorporate material and loading uncertainty in a level set framework for robust topology optimization.”
• Concurrent topology optimization of multiscale structures with multiple porous materials under random field loading uncertainty (2017, methodology): “(2010) have developed a generalized approach where K-L (Karhunen-Loeve) expansion and UDR Gauss-type quadrature were used for efficient uncertainty characterization and propagation (Chen et al. 2010).”
• Topology optimization based on moving deformable components: A new computational framework (2014, result): “12 can also be dealt with in a relatively direct way compared to existing approaches [16-18].”
• Robust topology optimization accounting for misplacement of material (2012, methodology): “Uncertain material properties have been considered by Asadpoure et al. (2011) in the optimization of truss structures and Chen et al. (2010) applied random fields to model spatially varying material properties in continuum topology optimization.”
• An efficient approach to reliability-based topology optimization for continua under material uncertainty (2015, background): “Most of the research in this direction has focused on robust topology optimization (RTO) under uncertainty (Guest and Igusa 2008; Chen et al. 2010; Asadpoure et al. 2010, 2011; Jalalpour et al. 2011; Schevenels et al. 2011; Lazarov et al. 2012a, b; Tootkaboni et al. 2012; Jang et al. 2012; Jansen…”