A Survey of Modeling of Lattice Structures Fabricated by Additive Manufacturing
By Guoying Dong, Yunlong Tang, Yaoyao Fiona Zhao. 2017
303 citations recorded by the source corpus.
Subfields: Automotive Engineering, Building and Construction, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/00e5cd01017b945f556d47111cce265af634ee4b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Jiping Lu
- Chengyi Pan
- Yafeng Han
- Xiaobing Feng
- Ifeanyichukwu Echeta
- Samanta Piano
- Guoying Dong
- Yunlong Tang
In Building and Construction
In Mechanical Engineering
- Jiping Lu
- Chengyi Pan
- Yafeng Han
- Xiaobing Feng
- Ifeanyichukwu Echeta
- Samanta Piano
- Guoying Dong
- Yunlong Tang
Citations in the corpus
- Mechanical anisotropy of additively manufactured stainless steel 316L: An experimental and numerical study (2021, background): “Many applications like topology optimization [33,34], modeling of lattice structures [35], and simulation of thermophysical processes during additive manufacturing [36] are heavily influenced by the anisotropy.”
- Review of defects in lattice structures manufactured by powder bed fusion (2019, methodology): “Additionally, incorporating defects into existing lattice structure models will be useful for quantifying the influence of defects on the desired function of the lattice [133].”
- Design and Optimization of Lattice Structures: A Review (2020, methodology): “Using homogenization to optimize lattice structures can be divided into two steps [23].”
- Design and Optimization of Graded Cellular Structures With Triply Periodic Level Surface-Based Topological Shapes (2019, background): “Among them, an artificially designed cellular structure possesses many superior properties that make it a promising solution for various applications, such as improved stiffness [3], heat transfer [4,5], energy absorption [6], and so on.”
- Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems (2020, background): “With the growth of data resources [18], a promising direction is to generate a database of the metamaterial microstructures and then utilize it to enable efficient data-driven design of a heterogeneous system [19-28].”