Design recovery of internal and external features for mechanical components
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
Contemporary reverse engineering (RE) tools focus on generation of free-form shapes from point cloud data collected from scanning systems. The final model contains a set of surfaces and curves that have no functional meaning, and noise due to manufacturing variations or wear are contained within the model. A different approach is required in order to create a more suitable model for engineered components because of these issues. To meet these challenges, a systematic approach is adopted in a comprehensive manner to extract the relevant information and transform it into pertinent design knowledge. A modular design recovery framework is presented that captures the component's structure, function and feature information at varying perspectives. To complement the framework, form recovery algorithms have been developed to transform point cloud data into wire frame geometry consisting of standard line and arc elements. Once the points are converted into curve primitives, adjustments are made to capture the design intent using heuristics, and common shapes and two-dimensional (2D) patterns are detected. From this geometry, a surface or solid model can be constructed using established geometry creation tools. Several case studies are presented that illustrate the form recovery algorithms to highlight their merits.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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