Stiffness design method of Gyroid-based functionally graded lattice structures with variable porosity controlled by load path
Bibliographic record
Abstract
The design of functionally graded lattice structure (FGLS) with triply period minimal surface can better improve the specific stiffness, strength, and energy-absorption capacity. However, how to design the porosity distribution and accurately control structural stiffness is a challenging task. This paper presents a stiffness design method of Gyroid-based FGLS based on variable porosity controlled by load paths. The continuous space iterative subdivision search method is established to calculate the minimum distance from central coordinate point of the cell to the main load paths. Then the mapping relationship between minimum distance and cell porosity is established. The influence of the dispersion range of cell porosity on mechanical properties is further analyzed. It was found that the mechanical properties increase with the increase of dispersion range. Next, cantilever beam and three-point bending beam are taken as the research object, and the FGLSs of the two models with bidirectional gradient are redesigned. The simulation and experimental results show that the stiffness of bidirectional gradient FGLSs is better than uniform density lattice structure under the same volume fraction. This method can accurately and explicitly controls the porosity of each cell by load path, thereby improving the overall structural stiffness.
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 | 0.000 |
| 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".