Microstructure and Thermomechanical Fatigue Behavior of Directionally Solidified Ni-Based Superalloys in OP Condition
Bibliographic record
Abstract
Two directionally solidified (DS) Ni-base superalloys, one with the GTD-111 composition and the other with a modified composition derived by numerical simulation, were produced using the Bridgman method. Solution and aging heat treatments were applied to the DS alloys to produce the desired microstructures. Thermomechanical fatigue (TMF) tests were conducted under fully reversed mechanical strain (R = −1) in the temperature range of 538–927°C in laboratory air. The tests were performed under the out-of-phase (OP) loading condition at the mechanical strain range of 0.8–1.5% with multiple specimens tested at each test condition to confirm the trend in fatigue lives. In general, the OP-TMF lives of the DS alloys depended on the applied mechanical strain range as well as the microstructural features of the alloys. By analyzing the fracture surface and longitudinal section of the tested specimens, the TMF lives of the DS alloys tested at mechanical strain ranges lower than 1.5% were found to be primarily affected by the coarse carbides formed in the interdendritic regions. In this study, the main damage mechanism of each alloy under the OP-TMF condition was elucidated in terms of its microstructural features, and recommendations were made to control the microstructures of the DS alloys to achieve enhanced TMF resistance.
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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".