Multiple-Material Topology Optimization of Compliant Mechanisms Created Via PolyJet Three-Dimensional Printing
By Andrew T. Gaynor, Nicholas A. Meisel, Christopher B. Williams, James K. Guest. 2014
274 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/0ed6adac2030d7768bc336d83c2d84124e85f4f8
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
- Fengwen Wang
- Ole Sigmund
- Boyan S. Lazarov
- Zhan Kang
- Krishnan Suresh
- Jikai Liu
- Anders Clausen
- Akihiro Takezawa
In Control and Systems Engineering
- Fengwen Wang
- Boyan S. Lazarov
- Jikai Liu
- Zhan Kang
- Andrew T. Gaynor
- Krishnan Suresh
- Albert C. To
- Lei Li
In Industrial and Manufacturing Engineering
- Ole Sigmund
- Lei Li
- Krishnan Suresh
- Lin Cheng
- Akihiro Takezawa
- Shikui Chen
- Boyan S. Lazarov
- Zhan Kang
Citations in the corpus
- Current and future trends in topology optimization for additive manufacturing (2018, methodology): “Gaynor et al. (2014) implemented both this scheme and a new alternative multi-material scheme in conjunction with a robust min-max topology optimization scheme to design multi-material compliant mechanisms (Sigmund 1997).”
- Design for Additive Manufacturing (2019, methodology): “Multi-material AM can be used to produce multi-material topology optimized structures like those described in [123][145][266][338][346].”
- Advanced robotics and additive manufacturing of composites: towards a new era in Industry 4.0 (2021, methodology): “developing field of AM, different multimaterial topology optimization methods have been implemented, such as SIMP, ESO, and level set.([335]) The SIMP method decreases the number of design variables.”
- Length scale and manufacturability in density-based topology optimization (2016, background): “Topology optimization for additive manufacturing has been demonstrated in a large number of articles [25], [137] and recently for multi material designs in [52],[136].”