Bibliographic record
Abstract
A complementary sensor technique for reverse engineering objects that are represented by a three-dimensional (3D) cloud data set is reported. The research focuses on objects whose surface form is manifest as a set of distinct free-form surface patches, each of which is enclosed by a boundary. The method incorporates three stages: (1) laser scanner-based digitization of all the free-form surface patches, (2) touch probe-based digitization of the surface patch boundaries, and (3) modeling of both data sets to create a complete B-spline curve and surface representation of the object. The patch boundary data, defined by the touch probe, is employed to segment the free-form surface data into the constituent patches. Furthermore, the boundary data is incorporated within a B-spline surface fitting process to constrain the boundaries. The two sensors functionally complement each other; the range sensor provides the required dense resolution of 3D points on the free-form surfaces whereas the touch probe accurately defines the patch boundaries. The method is ideal for objects comprised of both functional engineering features, e.g. bearing holes or precise mounting locators, and aesthetic features, such as hand grips or part covers. The touch probe is also ideal for digitizing boundaries where occlusion prevents the use of an optical digitizer. The laser-based sensor has an accuracy specification of 50 microns (over a 40-mm depth of field) whereas the touch probe is accurate to 4 microns over a 25-mm measurement length. An example part is modeled that has multiple free-form patches (defining the part’s outer cover) that require a large cloud data set for complete coverage. The corresponding patch boundaries accurately define the location of critical part mounting locations that require the touch probe’s precision.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".