# Infill Optimization for Additive Manufacturing -- Approaching Bone-like Porous Structures

arXiv: https://arxiv.org/abs/1608.04366 · HTML: https://mlsanity.com/paper/1608.04366/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2016.

## Built on by (most cited first, 3 of 3)

- [MIND: Microstructure INverse Design with Generative Hybrid Neural Representation](https://mlsanity.com/paper/2502.02607/) (2025)
- [Data-Driven Learning of 3-Point Correlation Functions as Microstructure Representations](https://mlsanity.com/paper/2109.02255/) (2021)
- [From Density to Geometry: YOLOv8 Instance Segmentation for Reverse Engineering of Optimized Structures](https://mlsanity.com/paper/2404.18763/) (2024)

## Abstract (arXiv)

Porous structures such as trabecular bone are widely seen in nature. These structures exhibit superior mechanical properties whilst being lightweight. In this paper, we present a method to generate bone-like porous structures as lightweight infill for additive manufacturing. Our method builds upon and extends voxel-wise topology optimization. In particular, for the purpose of generating sparse yet stable structures distributed in the interior of a given shape, we propose upper bounds on the localized material volume in the proximity of each voxel in the design domain. We then aggregate the local per-voxel constraints by their p-norm into an equivalent global constraint, in order to facilitate an efficient optimization process. Implemented on a high-resolution topology optimization framework, our results demonstrate mechanically optimized, detailed porous structures which mimic those found in nature. We further show variants of the optimized structures subject to different design specifications, and analyze the optimality and robustness of the obtained structures.