Elastic textures for additive fabrication
By J. Panetta, Qingnan Zhou, L. Malomo, Nico Pietroni, Paolo Cignoni, Denis Zorin. ACM Transactions on Graphics, 2015
297 citations recorded by the source corpus.
We introduce elastic textures: a set of parametric, tileable, printable, cubic patterns achieving a broad range of isotropic elastic material properties: the softest pattern is over a thousand times softer than the stiffest, and the Poisson's ratios range from below zero to nearly 0.5. Using a combinatorial search over topologies followed by shape optimization, we explore a wide space of truss-like, symmetric 3D patterns to obtain a small family. This pattern family can be printed without internal support structure on a single-material 3D printer and can be used to fabricate objects with prescribed mechanical behavior. The family can be extended easily to create anisotropic patterns with target orthotropic properties. We demonstrate that our elastic textures are able to achieve a user-supplied varying material property distribution. We also present a material optimization algorithm to choose material properties at each point within an object to best fit a target deformation under a prescribed scenario. We show that, by fabricating these spatially varying materials with elastic textures, the desired behavior is achieved.
Subfields: Automotive Engineering, Computational Mechanics, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/6d1e285059e47890d965bd194b7cc2337f48f2cf
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
In Computational Mechanics
In Mechanical Engineering
Citations in the corpus
- Infill Optimization for Additive Manufacturing—Approaching Bone-Like Porous Structures (2016, unknown): “The assembly of shapes from manufacturable micro-structures has been investigated in computer graphics [17], [18], [19] and topology optimization (e.”
- Exploiting Additive Manufacturing Infill in Topology Optimization for Improved Buckling Load (2016, background): “[23], for example, reports that the use of homogenized properties may be a reasonable approximation even when using a few unit cells.”
- 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].”