A geometric modelling framework to support the design of heterogeneous lattice structures with non-linearly varying geometry
Bibliographic record
Abstract
Abstract Geometric modelling has been a crucial component of the design process ever since the introduction of the first computer-aided design systems. Additive Manufacturing (AM) pushes design freedom to previously unachievable limits. AM allows the manufacturing of lattice structures which are otherwise close to impossible to be manufactured conventionally. Yet, the geometric modelling of heterogeneous lattice structures is still greatly limited. Thus, the AM industry is now in a situation where the manufacturing capabilities exceed the geometric modelling capabilities. While there have been advancements in the modelling of heterogeneous lattice structures, the review of relevant literature revealed critical limitations of the existing approaches. These limitations include their inability to model non-linear variation of geometric parameters, as well as the limited amount of controllable geometric parameters. This work presents a novel geometric modelling methodology based on function representation as an attempt to bridge this gap. The proposed approach avoids the manual definition of geometric parameters and provides a method to control them with mathematical functions instead. A software prototype implementing the proposed approach is presented, and several use-cases are analysed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".