CMM sequence optimisation with collision detection
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
This paper presents a method for finding optimal collision-free inspection sequences for Coordinate Measuring Machines (CMMs). The sequencing problem is formulated as a standard Travelling Salesperson Problem (TSP). During the network construction, collision detection is performed for each pair of measurement points using a novel image-based collision detection method. Penalties are added to path segments with collisions, and the nearest-neighbour TSP algorithm is applied. For the path segments with collisions, a heuristic algorithm is employed to make a detour around the interference volume. The proposed methods are implemented using the ACIS solid modelling kernel and OpenGL graphics library. The effectiveness of these methods is verified by simulations to demonstrate the collision-free path generation of parts with complex geometry and a comparison of the TSP solutions with and without the collision penalties is presented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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