P.: “Reproducibility and reliability of NDT phased array probes-part 2 -16-th WCNDT
Bibliographic record
Abstract
Abstract: Over the past few years, new procedures involving phased array technology were implemented through a growing number of NDT inspections. As this technology is now in production, requirements on the systems and on the transducers are increasing, especially about reliability and reproducibility Imasonic has developed phased array probes for industrial applications for more than 13 years, and its phased array technology early took into account these requirements. Some examples will be given based on internal quality records and also based on the feed back from Ontario Power Generation which has a large experience in Turbine inspection with phased array. Since 1996, OPG implemented industrial inspection procedure for turbine inspection using more than 150 phased array probes. Quality records from OPG will also be presented to illustrate phased array probes capabilities in terms of reproducibility and reliability OPG results include data for a batch of probes (3-17) manufactured on the same design requirement, but in different years. The following data will be presented: Gain sensitivity for best detection angle, S/N, sizing capability, Fc, BW, PD, beam features (focal depth, focal length-depth of field, focal beam dimensions) for L-wavesand T-waves. Introduction: The phased array technology is based on multi element transducers, having typically 16 to 128
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.006 |
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".