Bioinspired Stochastic Design: Tough and Stiff Ceramic Systems
Bibliographic record
Abstract
Abstract Ceramics possess desirable stiffness, compressive strength, and thermal properties compared to alternative material classes. Despite this, the adoption of ceramics into advanced industries has been hindered by their inherent brittleness. Using a state‐of‐the‐art manufacturing platform, the authors incorporate disordered microstructural features inspired by those found in natural, impact‐resistant organisms using tessellated ceramic cells. To precisely mimic these natural patterns, a disorder parameter is introduced to modulate the stochasticity of the ceramic architectures. By modifying simple geometrical features such as cut depth and the disorder parameter, the energy absorption is dramatically improved, and the stiffness of the system can be tailored. It is found that the stochastic designs exhibit elevated damage tolerance, denoted by higher dynamic energy absorption (up to 330% for the 3rd impact) and stiffness (up to 200% for the 3rd impact) than both monolithic and perfectly hexagonal architectures. The results show a superior multi‐hit resistance owing to optimal cut depth and stochasticity which give access to extrinsic toughening mechanisms that can be influenced through design parameters. This highly‐scalable, digital manufacturing platform for creating numerically programmable architectures propels the automated production of intelligent, high‐performance, and tailorable ceramic systems for industrial applications in aerospace, protective devices, and medicine.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".