C2 Approximation of Planar Curvilinear Profiles by Cubic B-Splines
Bibliographic record
Abstract
This paper presents a contribution to the automation and integration of topology optimization methods (TOM) with CAD, in the context of the design of statically loaded mechanical structures and parts.Starting from an initial CAD model with relevant engineering data, the goal is automatically generating an optimized CAD model with respect to engineering objectives and constrains.Though many optimization methods are now available, their complete and efficient integration into the design process faces several problems.After introducing the basic steps involved in the whole process and identifying the challenges inherent to this integration, this paper presents our contribution in addressing these challenges.The paper is focused on the specification of design and non-design sub-domains, on automatic mesh generation problems induced and on the adaptation of TO concepts in the context of 3D unstructured meshes.TO itself is adapted from a SIMP scheme, which is an arbitrary choice as any other optimization method could also have been used.Sets of results are presented to illustrate the potential and difficulties inherent to integrating TOM into the product design process with CAD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".