Simulation of Elastic Properties of Solid-lattice Hybrid Structures Fabricated by Additive Manufacturing
Bibliographic record
Abstract
The lattice structure is promising in a variety of engineering applications because of its unique mechanical properties. To satisfy certain functional requirements, lattice structures combined with the skin and solid are preferred in many cases. Additive Manufacturing (AM) has reduced the difficulty in fabricating Solid-Lattice hybrid structures, which brings more potential for applications. However, analyzing such a complex structure is challenging for traditional methods. In this paper, a new simulation model is proposed to reduce the computational cost and avoid poor mesh quality in simulating elastic properties of Solid-Lattice hybrid structures by Finite Element Analysis. The connecting area of the lattice strut and the solid is investigated to determine the best parameter for the new simulation model. A structure is designed and the experiment is conducted to validate the proposed method. A comparison between the new simulation model and the traditional one shows that the computational cost is dramatically decreased and the mesh quality is improved by the proposed method. And both of the simulation results are close to the experimental result which can be used to predict the mechanical performance of Solid-Lattice hybrid structures.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".