We propose a topology optimization (TO) framework where the structure is parameterized by a set of convex polygons. Extending feature mapping methods in TO, the representation allows for direct extraction of the geometry for further post processing. In addition, the method allows one to impose geometric constraints directly on the polygons that are otherwise difficult to impose in density or level set based approaches. The use of polygons provides for more more varied shapes than simpler primitives like bars, plates, or circles. The polygons are defined as the feasible set of a collection of halfspaces. Varying the halfspace's parameters allows for the expression of diverse set of configurations of the polygons. Furthermore, the halfspaces are differentiably mapped onto a background mesh to allow for analysis and gradient driven optimization.
@article{chandra2023PolyTO,
author = {Aaditya Chandrasekhar},
title = {PolyTO: Structural Topology Optimization with Convex Polygons},
journal = {arXiv preprint arXiv:2305.04406},
year={2023}
}