A facile method to create continuum stochastic sheet-based cellular materials
Bibliographic record
Abstract
While sheet-based lattices are both beautiful and advantageous from an engineering perspective they also present challenges in both design and production. Due to impressive mechanical and thermal properties, they should find application, but their usage is impeded by limited tools allowing them to be tailored. The interest in sheet-based lattices is driven, in part, by the emergence of additive manufacturing which allows them to be realised efficiently. Prior to this many of the exotic structures which are now easily producible were confined to description only by mathematical formula. In deploying sheet-based lattices it would be useful to have means to mimic the stochasticity observed in nature and engineer long range structures which do not suffer from discontinuities. In ‘assembling’ unit cells of different dimensions it is inevitable that the structure will not obey the mathematical definition of the lattice. Without remediation this can result in structures which cease to be useful as the discontinuities dominate performance. This is particularly challenging when seeking to create lattices with a degree of randomness or stochasticity. Here, a new method is proposed to create randomness in sheet-based lattices which makes use of power spectral density functions to describe any functional descriptor for a defined randomness profile. This allows structures to be manufactured which represent any randomness design intent. By exploiting the power spectral density function it is possible to limit the disparity between adjacent unit cells and as such unite disparate unit cells in a simple way and so overcoming marked discontinuities which will limit the performance of these lattice types.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".