The Potential of Acousto-Ultrasonic Techniques for Inspection of Baked Carbon Anodes
Why this work is in the frame
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Bibliographic record
Abstract
High quality baked carbon anodes contribute to the optimal performance of aluminum reduction cells. However, the currently decreasing quality and increasing variability of anode raw materials (coke and pitch) make it challenging to manufacture the anodes with consistent overall quality. Intercepting faulty anodes (e.g., presence of cracks and pores) before they are set in reduction cells and deteriorate their performance is therefore important. This is a difficult task, even in modern and well-instrumented anode plants, because lab testing using core samples can only characterize a small proportion of the anode production due to the costly, time-consuming, and destructive nature of the analytical methods. In addition, these results are not necessarily representative of the whole anode block. The objective of this work is to develop a rapid and non-destructive method for quality control of baked anodes using acousto-ultrasonic (AU) techniques. The acoustic responses of anode samples (sliced sections) were analyzed using a combination of temporal features computed from AU signals and principal component analysis (PCA). The AU signals were found sensitive to pores and cracks and were able to discriminate the two types of defects. The results were validated qualitatively by submitting the samples to X-ray Computed Tomography (CT scan).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 it