Correction of the systematic errors of 3D scanner measurements by datafusion with CMM data
Bibliographic record
Abstract
Technological advances in additive and subtractive manufacturing are making it easier to produce parts with complex geometries. When the design tolerances are in micrometers, the task of inspecting those parts is complex and expensive. Currently, no measuring instrument can perform both geometric and dimensional inspection with high resolution and high accuracy. This work considers a coordinate measuring machine (CMM) and a 3D scanner. A CMM can take measurements of high accuracy but with a low density of points, and the 3D scanner can take measurements of low accuracy but with high density. This project aims to exploit each instrument's strengths with a data fusion technique to perform a geometric and dimensional inspection of complex machined parts. The result of the fusion should be a high-accuracy, high-density mesh representation of the measured part. A data fusion technique that uses a 3D scanner's high-density mesh representation, corrected by a non-rigid transformation to fit CMM points as a reference is proposed. The algorithm is a version of the Non-Rigid Iterative Closest Point algorithm with a point-tosurface distance metric. It allows for global deformations to correct systematic errors but not local ones, to preserve small details. The method has been validated on part samples and shows promise in correcting systematic errors in 3D scanner measurements.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".