Automatic modification retrieval between CAD parts
Bibliographic record
Abstract
Several research works have been focused on integrating FEA (finite-elements analysis) with CAD (Computer Aided Design) over the last decade. In spite of the improvements brought by this integration, research work remains to be done in order to better integrate all the operations led during the design process. Until now, the communication between CAD modules remains static. The design process involves several modifications of an initial design solution. Consequently, there is a need for more flexible communications between CAD modules through the design cycle. Some approaches have been developed in order to reduce the design process length when using FEA, and to automate the transfer of part’s data from one step of the process to the next one. Automatic re-meshing is one of these approaches and it is based on automatically updating the part’s mesh around modifications zones, in the case of a minor change in the part’s design, without the need to re-mesh the entire part. The purpose of this paper is presenting a new tool, aiming at the improvement of automatic re-meshing procedures. It basically consists in automatically identifying and locating modifications between two CAD parts (typically an initial design and a modified design). A major benefit of the approach presented here is that it is completely independent of the description frame, which is made possible with the use of vector-based geometric representations.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".