The Coherent Point Drift Algorithm Adapted for Fixtureless Metrology of Non-rigid Parts
Bibliographic record
Abstract
Unlike the metrology of rigid parts, no viable and industrial solutions in the case of non-rigid parts are available. Due to gravity load and residual stress, non-rigid parts (flexible, compliant) may have in a Free State condition a significant different shape than their corresponding nominal geometry (CAD model). As a result, very expensive and specialized fixtures mounting are needed by the industry to constrain the component during the inspection. Dealing with this real industrial problem, this paper proposes a new method to inspect non-rigid parts without these specialized fixtures. In this method, the CAD model is smoothly modified to fit the scanned part respecting two criteria that belong to non-rigid parts. The first criterion is the isometric transformation (or the condition that stretch should be very small) between the original CAD model and the modified one. The second criterion is the Euclidian distance between the modified CAD model and its corresponding scanned part. The proposed approach consists of adapting the Coherent Point Drift powerful non rigid registration method to meet the specifications of non-rigid parts. In other words, by minimizing the two above criteria, the paper proposes a ‘flexible’ registration to align the scanned manufactured compliant part to its nominal model in order to compare them and to deliver an inspection report. Satisfying results were obtained when validating the proposed method on a case study taken from the aerospace industry. The low percentage of error between the estimated value of defect and the reference one reflect the effectiveness of the proposed approach.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".