5-Axis In-Line Measurement System for Laser Materials Processing
Bibliographic record
Abstract
The dimensional and positional information is very important for laser materials processing of components, especially for laser cladding-based additive manufacturing. Therefore, inspection of the component based on its original CAD model is essential to ensure the component meeting the geometrical requirements. In general, the processed component has to be dismounted from the laser fabrication system before it can be inspected, which is time-consuming and may introduce alignment error from datum. In this paper, a 5-axis in-line measurement system is introduced, in which a non-contact laser measuring method along with CAD/CAM software is integrated to a laser materials processing system. An algorithm has been developed to automatically generate NC programs for measurement and comparison for outside features of components. Therefore, the inspection can be performed just after the component has been fabricated on the same system. Comparing to a 3-axis measuring system we reported before, the developed 5-axis inline measurement system provides much more flexibility, accessibility and accuracy for performing measurement on site. The developed in-line measuring capability can extend the functionality of conventional laser materials processing system and significantly shorten inspection time.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".