Capturing and analysing how designers use CAD software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current Computer-Aided Design (CAD) packages support the storage of the final design models and solutions in different formats, and PLM software manages the high-level information about the design process, such as the versioning of the design solutions. However, the processes happening inside the CAD software are not being fully captured. Information such as the sequence of actions (create a sketch, set a distance constraint, remove a pocket, modify the diameter of a through hole, etc.), versioning of the created objects, etc. is missing. This information can be used to understand how a designer uses CAD software to generate geometric representations. In design companies, capturing this information during a product design project would help to evaluate the designer’s way of working with CAD software. In design education, collecting information on how design students generate geometric representations would allow teachers to identify the areas of misunderstanding, improve the education process by representing the optimal way of working, and help teachers to correctly evaluate their students’ performance in using CAD software. This paper proposes a framework to support an analysis of how designers use CAD software to generate geometric representations. This framework consists of structured models and an approach which guides the actor in capturing the design process. We use CATIA as a CAD software solution, but the proposed approach is generic and can be extended to any CAD software. The validity of the proposed approach is illustrated through a case study.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it