Bibliographic record
Abstract
In recent years, laser scanning has been applied in the manufacturing industry as a tool for inspection and 3-D digitization as well as in reverse engineering. It has many advantages compared with the traditional contact measurement techniques, such as coordinate measuring machines (CMM). In order to perform laser scanning more efficiently, automated laser scanning planning needs to be developed based on the CAD model of a given part. This research presents a computer-aided planning method for laser scanner based on CAD model of the part. The method integrates three planning criteria, namely visibility, efficiency, and accuracy into the planning system. A feature differentiation method, based on the ray tracing algorithm, is proposed and applied to detect steep walls of certain deep concavity features, such as slots, holes, and pockets, which are very hard to reach by laser scanning but are suitable for CMM probing. The proposed methods and algorithms have been implemented and integrated into a Computer-Aided Laser-scanning Planner (CALP) for inspection applications. An artifact block with five planar surfaces is used to test the scanning plan which is generated from the proposed methods. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .Y35. Source: Masters Abstracts International, Volume: 41-04, page: 1172. Adviser: Hoda Elmaraghy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2002.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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".