Biomimetic bi-material designs for additive manufacturing
Bibliographic record
Abstract
Superior material properties have been recently exhibited under the concept of biomimetic designs, where the material architectures are inspired by nature. In this study, a computational framework is developed to present novel architectured bi-material structures with tunable stiffness, strength, and toughness to be used for additive manufacturing (AM). The structure of natural nacre is mimicked to design robust multilayered structures constructed from hexagonal brittle and hard building blocks bonded with soft materials and supports. A set of computational models consisting of fully bonded zones, while allowing for interlayer interactions are created to accurately mimic the interplay between the hard and soft organic phases. As required for such complex designs, the numerical constraints are properly set to run quasi-static non-linear explicit analysis, which allow for a 3× faster analysis with higher efficiency and 2× lower computational cost, when compared to static analysis. The models are used to assess the stiffness, strength and toughness of bi-material beams when subjected to a flexural three-point bending load. The influence of structural features like the soft-to-hard volume ratio (i.e. the distance between each building block, its aspect ratio, and overlap length), material features (e.g. the stiffness ratio of the hard-to-soft phases), the plastic strain failure of soft phase, and AM features (e.g. different types of within-layer/sandwiched supports) are systematically investigated. The results revealed that the toughness of the architectured beams was enhanced by up to 25% when compared to a monolithic structure. This improvement is due to the frictional tile sliding in the brittle phase and the extensive shear plastic deformation of the soft interfaces. This work provides compatible designs to facilitate the AM of nacre-based bi-martial structures with balanced/tailored mechanical performance and to understand the influence of the architectural parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".