Non-destructive detection of the useful zone in Boron carbide-reinforced metal matrix composites laminated plates
Bibliographic record
Abstract
Boron carbide (B4C)-reinforced metal matrix composites (MMCs) are materials of choice for the manufacturing of radioactive material containers due to their neutron absorption properties. The neutron absorption capacity of this material depends mainly on the density of B4C particles in the aluminum matrix. The reinforced MMC sheets used to manufacture these containers are produced by a lamination process. The raw sheets coming out of the rolling mill show uneven distribution of B4C particles between the extremities of the sheets, constituted solely of aluminum (rejection zone), and the interior of the laminated sheet, with the right aluminum / B4C ratio (useful zone). Detecting the boundary between the useful zone and the rejection zone is a crucial step in the process, as it defines which part of the sheet should be used to manufacture the containers. Determination of this boundary is currently carried out by chemical digestion, which is costly, time-consuming, and provides only localized information. The aim of this work was to develop a non-destructive technique able to detect the boundary between the rejection zone and the useful zone. The approach is based on the difference in acoustic attenuation between rejection and useful zones. More precisely, the ultrasonic parameter used was the amplitude ratio between the backwall and the front wall echo of the sheet when performing a pulse-echo inspection at 20 MHz. The amplitude ratio in aluminum is smaller than in the useful zone containing B4C, which is more attenuating for the ultrasounds. Two types of scans were performed: 1D linear scan and 2D scan. The linear scan was performed using a conventional probe coupled to the part via a conformable wedge. The boundary between both zones was detected by segmenting the amplitude ratio profile into different sections with constant statistical parameters. The 2D scan was performed with an automated 5-axis inspection system in a water tank using a conventional probe. A map of amplitude ratios was generated, and the useful zone was detected using an agglomerative clustering segmentation. Validation was carried out by comparing the amplitude ratio profile with chemical digestion data from samples taken from these sheets. Experimental validation, performed on sheets with different B4C densities and thicknesses, showed that amplitude ratio enables a good detection of the boundary and therefore the useful zone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".