# Characterization of fracture in topology-optimized bio-inspired networks

## TL;DR

This study uses topology optimization and network analysis to understand fracture mechanisms in bio-inspired structures modeled after trabecular bone, revealing how design objectives influence structural robustness.

## Contribution

It introduces a combined computational approach using topology optimization and graph theory to analyze fracture behavior in bio-inspired networks.

## Key findings

- Objective weights significantly affect fracture locations.
- Network analysis identifies critical regions prone to failure.
- Optimized structures show varied responses to mechanical loading.

## Abstract

Designing strong and robust bio-inspired structures requires an understanding of how function arises from the architecture and geometry of materials found in nature. We draw from trabecular bone, a lightweight bone tissue that exhibits a complex, anisotropic microarchitecture, to generate networked structures using multi-objective topology optimization. Starting from an identical volume, we generate multiple different models by varying the objective weights for compliance, surface area, and stability. We examine the relative effects of these objectives on how resultant models respond to simulated mechanical loading and element failure. We adapt a network-based method developed initially in the context of modeling trabecular bone to describe the topology-optimized structures with a graph theoretical framework, and we use community detection to characterize locations of fracture. This complementary combination of computational methods can provide valuable insights into the strength of bio-inspired structures and mechanisms of fracture.