Review of Ultrasonic Phased Arrays for Pressure Vessel and Pipeline Weld Inspections
Bibliographic record
Abstract
Major improvements in weld inspection are obtained using Phased Array technology with capability for beam steering, electronic scanning, focusing, and sweeping the ultrasonic beams. Electronic scanning is much faster than raster scanning, and can optimize angles and focusing to maximize defect detection. Pressure vessel (PV) inspections typically use “top, side, end” or “top, side, TOFD” views, though other imaging is possible. Special inspections can be performed, e.g., for specific defects, or increased coverage. Defects can be sized by pulse-echo as per code, by time-of-flight Diffraction or by back diffraction. New PV inspection codes, particularly ASME Code Case 2235, permit the use of advanced ultrasonic inspection techniques. Pipeline girth weld inspections use a unique inspection approach called “zone discrimination,” and have their own series of codes. While similar equipment is used in pipeline as in PV inspections, the pipeline philosophy is to tailor the inspection to the weld profile and predicted lack of fusion defects. Pipeline displays are specifically designed for near real-time data analysis. Both ASME CC 2235 and the pipeline codes permit the use of Fitness-For-Purpose, which reduces construction costs. Overall, phased array systems meet or exceed all PV and pipeline codes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".