Short fatigue crack growth sensitivity to thermo-mechanical fatigue loading
Bibliographic record
Abstract
Most high-temperature components are subject to out-of-phase thermomechanical fatigue (OP-TMF), which induces crack growth at low temperatures. However, OP-TMF has been little studied in the context of short cracks. This study focuses on the experimental sensitivity of OP-TMF loading conditions playing on temperature range, gradient, and dwell time for thin sheet superalloy specimens. The material of interest is a Co-based superalloy, HA188. It is widely used in combustion chambers. The experimental analysis is based on full-field measurements for temperature, strain and damage by infrared thermography, digital image correlation and high resolution images from 300 to 900 °C. The main conclusion is that the temperature gradient, together with the temperature amplitude, largely determines the strain amplitude and subsequent fatigue crack growth rate (FCGR) of short cracks. In situ measurements of damage and crack closure were obtained using supervised machine learning based on images. This clarifies that crack closure is only partial and that the crack network growth rate is consistent with the individual short crack growth rate. Finally, 3D finite element analysis considering realistic temperature field and strain energy based FCGR model was able to evaluate the fatigue life in this context. It is shown that the OP-TMF FCGR is very close to the FCGR of the maximum temperature of the TMF cycle due to partial crack closure. • TMF loading parameters have been varied in both temperature amplitude and gradient. • These temperature parameters drive the strain amplitude. • Short crack growth is sensitive to strain amplitude for a given maximum temperature. • Supervised machine learning yields access to in situ crack closure analysis. • Fatigue crack growth rate is similar for OP-TMF and maximum temperature isothermal testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".