Phased array probe for the inspection of large steel forgings
Bibliographic record
Abstract
Reliable inspection of large steel forgings is a significant problem across multiple industries. When the dimensions of forgings are at least 1000 mm in each direction, ultrasonic phased array inspection becomes difficult because conventional probes are not designed to transmit and focus acoustic energy over such large distances. Using simulations and experiments, this article aims to discuss the possibilities and limitations of phased array imaging of large forged steel ingots. Simulations were used to design a transducer based on the reflected amplitude, as well as the lateral and axial resolutions of defects in a 1000 mm thick block. It was shown that when limiting the aperture to allow easy handling in an industrial inspection setting, the lateral resolution and the reflected amplitude were the main limitations. Increasing the element width maximizes the energy transmitted into the material, but it also moves elements away from each other, thus reducing the lateral resolution. Based on the simulations, an experimental phased array transducer was manufactured with eight elements of 9.25 × 22.5 mm. The probe was used to perform measurements and generate total focusing method images of three different at a depth of 370 mm in a 776 mm forged steel block.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".