Design Method for Lattice-Skin Structure Fabricated by Additive Manufacturing
Bibliographic record
Abstract
Parts with complex geometry structure can be produced by AM without significant increase of fabrication time and cost. One application of AM technology is to fabricate customized lattice-skin structure which can enhance performance of products with less material and less weight. However, most of traditional design methods only focus on design at macro-level with solid structure. Thus, a design method which can generate customized lattice-skin structure for performance improvement and functionality integration is urgently needed. In this paper, a novel design method for lattice-skin structure is proposed. In this design method, FSs and FVs are firstly generated according to FRs. Then, initial design space is created by filling FVs and FSs with selected lattice topology and skin, respectively. In parallel to the second step, initial parameters of lattice-skin structure are calculated based on FRs. Finally, TO method is used to optimize parameter distribution of lattice structure with the help of mapping function between TO’s result and lattice parameters. The design method proposed in this paper is proven to be efficient with case study and provides an important foundation for wide adoption of AM technologies in industry.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".