A Laser Thermal Cycling Rig as a New Method to Characterize the Evolution of Coating Adhesion under Thermal Cycle
Bibliographic record
Abstract
Abstract Thermal sprayed coatings are often used for high temperature applications and, per se, are subjected to transient temperature gradients during operation. The recurrent temperature changes generate stresses that damage the coating with time, and can even lead to its delamination. The most common methods to evaluate coating behavior under thermal cycling are furnace testing or burner rigs. Both approaches cannot match the conditions reached in service for several applications, in terms of the achievable heating rates for instance. As a consequence, a versatile and robust method to evaluate coating resistance to spalling under thermal cycles is still to be found. This paper presents the development of a thermal cycling rig where the heat input is provided by a laser. This rig allows easy testing of several samples jointly for heating rates as high as 55°C/s and for thousands of thermal cycles. Preliminary trials have allowed the development of different spalling criteria. Finally, it was found that SS430-based materials arc-sprayed on Al substrates exhibit higher delamination resistance (life) under rapid heating/cooling cycles than SS304 coatings on the same substrate. For such high heating rates, the thermal stresses generated in the coating would be more critical than the thermal mismatch at the interface coating/substrate.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".