Damage evolution in thermal barrier coating under thermal gradient mechanical fatigue loading
Bibliographic record
Abstract
This research aims to clarify the driving forces behind interfacial damage in thermal barrier coatings associated with buckling-driven delamination. Using an artificial interfacial defect processed by laser shock, thermomechanical fatigue loadings are investigated both with or without a through-thickness temperature gradient. In situ infrared imaging enables the tracking of further debonding, allowing assessment of the influence of complex loading conditions on the interfacial damage rate. Based on these findings, a clear ranking of the influence of thermomechanical fatigue parameters is established, including temperature, temperature gradient, cooling rate, strain level, and stress relaxation during dwell time at maximum temperature. A sensitivity analysis was carried out using a finite element method, considering temperature gradients and the realistic geometry of the blister. Through-thickness gradients were shown to increase the maximum stress intensity factors at the interface, driving monotonic damage of the interface. The interface temperature and the local strain amplitude govern the stress intensity factor amplitude and subsequent interfacial toughness decrease, driving fatigue damage of the interface. • Thermal barrier coatings with processed blisters undergo thermomechanical gradient fatigue tests. • Interfacial damage rate is monitored in situ by infra red thermography. • Through thickness gradients is observed to yield local ratchetting. • Thermal gradients and mechanical loadings drive interfacial monotonic damage. • Fatigue damage induces decrease in interfacial toughness.
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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 | 0.000 | 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) | 0.001 | 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".