Optimization of 3D network topology for bioinspired design of stiff and lightweight bone-like structures.

A truly bioinspired approach to design optimization should follow the energetically favorable natural paradigm of "minimum inventory with maximum diversity". This study was inspired by constructive regression of trabecular bone – a natural process of network connectivity optimization occurring early in skeletal development. During trabecular network optimization, the original excessively connected network undergoes incremental pruning of redundant elements, resulting in a functional and adaptable structure operating at lowest metabolic cost. We have recapitulated this biological network topology optimization algorithm by first designing in silico an excessively connected network in which elements are dimension-independent linear connections among nodes. Based on bioinspired regression principles, least-loaded connections were iteratively pruned upon simulated loading. Evolved networks were produced along this optimization trajectory when pre-set convergence criteria were met. These biomimetic networks were compared to each other, and to the reference network derived from mature trabecular bone. Our results replicated the natural network optimization algorithm in uniaxial compressive loading. However, following triaxial loading, the optimization algorithm resulted in lattice networks that were more stretch-dominated than the reference network, and more capable of uniform load distribution. As assessed by 3D printing and mechanical testing, our heuristic network optimization procedure opens new possibilities for parametric design. [Display omitted] • This is a bioinspired optimization algorithm to design 3D lattice structures. • The optimized network is a scale- and material-independent design blueprint. • Properties of a developing trabecular bone network are simulated and replicated. • Iterative network topology optimization can target different convergence criteria. • Mechanical behavior of optimized networks can be fine-tuned by parametric embodiment. [ABSTRACT FROM AUTHOR]