Inspection of Prebaked Carbon Anodes Using Multi-Spectral Acousto-Ultrasonic Excitation
Bibliographic record
Abstract
Reduction cell operation in primary aluminum production is strongly influenced by the properties of baked anodes. Producing consistent anode quality is more challenging nowadays due to the increasing variability of raw materials. Taking timely corrective actions to attenuate the impact of raw material fluctuations on anode quality is also difficult based on the core sampling and characterization scheme currently used by most anode manufacturers, because it is applied on a very small proportion of the anode production (about 1%), and long-time delays are required for lab characterization. The objective of this work is to develop rapid and non-destructive methods for the inspection of baked anodes. Previous work has established that sequential excitation of smaller parts collected from an industrial sized anode using acousto-ultrasonic signals at different frequencies allowed detecting and discriminating anode defects (pores and cracks). This was validated qualitatively using X-ray computed tomography. This work improves the method by using frequency-modulated excitation and building quantitative relationships between the acousto-ultrasonic signals and defects extracted from tomography images using Wavelet Transforms and Partial Least Squares (PLS) regression. The new excitation approach was found to provide similar or better inspection performance compared with sequential excitation, while requiring a shorter cycle time.
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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 | 0.001 | 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) | 0.001 | 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".