Automatic sampling for CMM inspection planning of free-form surfaces
Bibliographic record
Abstract
Coordinate measuring machines (CMMs) are used to examine the conformity of the produced parts with the designer's intent. 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. Efficient and reliable approaches to determine the locations of the points to be sampled from free-form surfaces using the CMM were developed. Four heuristic algorithms for sampling based on the NURBS features of free-form surfaces are presented. The sampling criteria are equiparametric, surface patch size and the surface patch mean curvature. An algorithm for automatic selection of sampling algorithms performs complexity checks on NURBS surfaces, including the surface curvature changes and surface patch size changes, and selects the suitable sampling algorithm. Extensive simulations were performed using the developed methodologies to evaluate their performance using free-form surfaces with different degrees of complexity and compared with the uniform sampling pattern. The CMM measurement errors and manufacturing form errors have been simulated in these studies. The developed algorithms provide a useful tool in selecting the effective sampling plans for the tactile CMM inspection planning of free-form surfaces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".