Mimicking bone remodeling to optimize hierarchical, multi-material 3D printed metamaterials
Source abstract
Abstract
• A bone-mimicking remodeling algorithm is introduced to iteratively optimize the distribution of soft and hard voxels, enhancing mechanical performance without altering the macrostructure. • The proposed method significantly improves strain energy uniformity, expands the achievable range of elastic properties, and increases mechanical strength by up to 78 % compared to initial designs. • The remodeling process optimizes material arrangement at the voxel level, maintaining overall structure and relative density while improving mechanical efficiency and stress distribution. In this study, by mimicking bone tissue adaptation, we introduce a hierarchical design approach to optimize the mechanical performance of auxetic metamaterials. Using a multi-material voxel-based 3D printer, we rationally position soft and hard phases at the voxel level based on a bone-inspired remodeling algorithm that maintains the homeostasis of strain energy density. This process introduces an additional length scale within the structure, leading to (1) a significant expansion of the envelope of achievable elastic properties, (2) more homogeneous strain energy distributions, and (3) up to 78 % stronger metamaterials compared to initial designs. Our results demonstrate that this bone-mimicking design approach enables the emergence of an intermediate length scale between the unit cell and voxel scales, which is responsible for the observed improvements in mechanical performance. Taken together, these findings highlight the potential of biomimetic remodeling for the rational design of stress-worthy, multiscale mechanical metamaterials that combine unusual elastic properties with high mechanical performance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Cellular and Composite Structures · Additive Manufacturing and 3D Printing Technologies · Bone Tissue Engineering Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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