The Use of Acoustic Emission Techniques for Characterizing Failure Mechanisms of Thermal Barrier Coatings Under Thermal Cycling Conditions
Bibliographic record
Abstract
Abstract Thermal barrier coatings are mainly used to protect underlying alloys from heat and chemical aggressions, especially in jet turbines and diesel engines. The challenge in TBCs’ applications is to use them to protect the upper moving turbine’s blades where their surface temperature can reach 1200°C. The limiting effect is the reliability of these coatings. The work described in this paper is a continuation of our earlier research work, which is focused on the use of Acoustic Emission Technique to assess the long-term behavior of thermal barrier coatings under thermal cycling conditions. Emphasis is placed in this presentation on the comparison of different signal processing techniques and the evaluation of their potential usefulness for the prediction of the coating behavior and failure modes. The work is carried out in parallel with a finite element modeling study of the thermal and stress distribution in the coating, which provides a valuable insight in the coating stress distribution prior to failure.
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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 | 0.000 | 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".