Mesoscale Lattice Structure Design and Simulation with the Support of a Property Database
Bibliographic record
Abstract
The lattice structure is a type of cellular materials [1] that has truss-like structures with interconnected struts and nodes in a three-dimensional (3D) space. Compared to other cellular materials such as random foams and honeycombs, the lattice structures exhibit better mechanical performance [2]. Some examples of lattice structures are shown in Figure 8.1. The first one is a randomized lattice structure. Due to the disordered lattice cells, the properties of this type of lattice structures are stochastic and difficult to control. But it can be used as implants in orthopedic surgeries. The second and the third are lattice structures with periodic unit cells. The difference is that the strut thickness of the second one is uniform, which is called homogeneous lattice structures. However, the third one has non-uniform strut thickness for specific loading conditions, which is called heterogeneous lattice structures. By properly adjusting the material in vital parts of the lattice structure, the heterogeneous periodic lattice structure can have a better mechanical performance than the homogeneous one with the same weight. Plenty of design and optimization methods [3-5] have been proposed for lattice structures to pursue better performance in different engineering applications. For example, the lattice structure is applied to achieve lightweight [3, 4], energy absorption [6], and thermal management [7]. Due to the complexity of the geometry, the fabrication of lattice structures had been the most critical issue. However, with the development of Additive Manufacturing (AM) processes, the difficulty in the fabrication was largely relieved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".