Continuous Optimization of Adaptive Quadtree Structures

TL;DR

This paper introduces a continuous optimization approach for designing quadtree structures that maximize mechanical stiffness, replacing discrete decisions with continuous variables to improve structural performance in 3D printing applications.

Contribution

It presents a novel continuous formulation for quadtree optimization, enabling more effective structural design by replacing discrete refinement choices with continuous variables.

Findings

Optimized quadtree structures are significantly stiffer than uniform patterns.

The continuous method outperforms heuristic discrete optimization.

Adaptive structures serve as lightweight infill for 3D printed parts.

Abstract

We present a novel continuous optimization method to the discrete problem of quadtree optimization. The optimization aims at achieving a quadtree structure with the highest mechanical stiffness, where the edges in the quadtree are interpreted as structural elements carrying mechanical loads. We formulate quadtree optimization as a continuous material distribution problem. The discrete design variables (i.e., to refine or not to refine) are replaced by continuous variables on multiple levels in the quadtree hierarchy. In discrete quadtree optimization, a cell is only eligible for refinement if its parent cell has been refined. We propose a continuous analogue to this dependency for continuous multi-level design variables, and integrate it in the iterative optimization process. Our results show that the continuously optimized quadtree structures perform much stiffer than uniform patterns and the heuristically optimized counterparts. We demonstrate the use of adaptive structures as lightweight infill for 3D printed parts, where uniform geometric patterns have been typically used in practice.