An Approach of Irregular Porous Structure Modeling Based on Subdivision and NURBS
Bibliographic record
Abstract
The use of 3D CAD in mechanical product design has become a standard practice.Consequently, methods and tools are continually being developed to improve designers' efficiency in the creation, modification and analysis of 3D CAD models.Recent software developments had led to the emergence of multiple tools capable of comparing 3D CAD models to locate shape similarities or differences, leading to benefits in various CAD-and PLM-related application domains such as design reuse, data exchange and engineering change management.This paper describes evaluation trials that were performed on sets of commercially available 3D CAD model comparison tools.The goal was to evaluate their capacity to efficiently calculate, represent and display 3D CAD model differences in shape change transposition scenarios where shape modifications must be precisely located and elaborated in order to be consistently propagated between application-specific models of a product.First, some basic concepts of 3D CAD model comparison are presented.Then, simulated shape change assessment scenarios are defined to pilot two series of evaluation trials intended for existing software tools capable of comparing 3D CAD procedural and explicit models, respectively.The results are summarized and conclusions are drawn.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".