Automatic sampling for CMM inspection planning of free-form surfaces
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
The advance in design, and manufacturing technologies has made it possible to design, and manufacture products with high degrees of irregularity, such as free form surfaces. Coordinate Measuring Machines (CMMs) are used to examine the conformity of the produced parts with the designer's intention. The inspection of free form surfaces is a difficult process due to their complexity, and irregularity. Many tasks are performed to ensure a reliable and efficient inspection using CMMs. Sampling is an essential and vital step in inspection planning. It is a major contributor to the CMM measurement uncertainty. This research focuses on developing efficient and reliable approaches to determine the locations of the points to be sampled from free form surfaces using the CMM. Four heuristic algorithms for the sampling of free form surfaces have been developed. Optimal sampling of free form surfaces using Genetic Algorithms has been introduced. An algorithm for the automatic selection of sampling algorithm based on the surface complexity is developed. The developed sampling algorithms have been implemented, and integrated into a computer-aided system for the sampling of free form surfaces. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .E45. Source: Masters Abstracts International, Volume: 40-03, page: 0764. Advisers: Hoda A. Elmaraghy; Waguih H. Elmaraghy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2001.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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