Mesoscale Lattice Structure Design and Simulation with the Support of a Property Database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it