Bibliographic record
Abstract
Lattice materials are characterized at the microscopic level by a regular pattern of voids confined by walls. Recent rapid prototyping techniques allow their manufacturing from a wide range of solid materials, ensuring high degrees of accuracy and limited costs. The microstructure of lattice material permits to obtain macroscopic properties and structural performance, such as very high stiffness to weight ratios, highly anisotropy, high specific energy dissipation capability and an extended elastic range, which cannot be attained by uniform materials. Among several applications, lattice materials are of special interest for the design of morphing structures, energy absorbing components and hard tissue scaffold for biomedical prostheses. Their macroscopic mechanical properties can be finely tuned by properly selecting the lattice topology and the material of the walls. Nevertheless, since the number of the design parameters involved is very high, and their correlation to the final macroscopic properties of the material is quite complex, reliable and robust multiscale mechanics analysis and design optimization tools are a necessary aid for their practical application. In this paper, the optimization of lattice materials parameters is illustrated with reference to the design of a bracket subjected to a point load. Given the geometric shape and the boundary conditions of the component, the parameters of four selected topologies have been optimized to concurrently maximize the component stiffness and minimize its mass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".