Interlocking design, programmable laser manufacturing and testing for architectured ceramics
Bibliographic record
Abstract
Tough and impact-resistant ceramic systems offer a wide range of remarkable opportunities beyond those offered by the conventional brittle ceramics. However, despite their promise, the availability of traditional manufacturing technique for fabricating such advanced ceramic structures in a highly controllable and scalable manner poses a significant manufacturing bottleneck. In this study, a precise and programmable laser manufacturing system was used to manufacture topologically interlocking ceramics. This manufacturing strategy offers feasible mechanisms for a precise material architecture and quantitative process control, particularly when scalability is considered. An optimized material removal method that approaches near-net shaping was employed to fabricate topologically interlocking ceramic systems (load-carrying assemblies of building blocks interacting by contact and friction) with different architectures (i.e., interlocking angles and building block sizes) subjected to low-velocity impact conditions. These impacts were evaluated using 3D digital image correlation. The optimal interlocked ceramics exhibited a higher deformation (up to 310%) than the other interlocked ones advantageous for flexible protections. Their performance was tuned by controlling the interlocking angle and block size, adjusting the frictional sliding, and minimizing damage to the building blocks. In addition, the developed subtractive manufacturing technique leads to the fabrication of tough, impact-resistant, damage-tolerant ceramic systems with excellent versatility and scalability.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".