Bibliographic record
Abstract
Precipitation hardened nickel-based superalloys are widely used in aero and industrial gas turbine engines due to their excellent high temperature strength and remarkable hot corrosion resistance. A drawback of many of these alloys is that they are very difficult to weld due to their high susceptibility to heat affected zone (HAZ) cracking, both during welding and post weld heat treatments (PWHT). Weld cracking in many of these alloys has been attributed mostly to constitutional liquatioin of grain boundary NbC precipitates. however, HAZ cracking has been observed in carbon-free superalloys as well, Therefore, research was initiated to examine grain boundary liquation and cracking in HAZs in a variety of Ni-based superalloys. It was found that intergranular cracking of grain boundaries involved liquation of several other phases, in addition to NbC, that were present in pre-weld microstructure of the alloy. These even included the primary strengthening phase,γ’, in a very widely used superalloy, Inconel 738. In addition, segregation of melting point depressant element B was also observed at gain boundaries in other superalloys, which also caused grain boundaries in HAZ to liquate and resulting in their cracking. An overview of microstructural aspects of different liquation phenomena involved and characteristics of the liquid film contributing to the HAZ microfissuring of nickel superalloys will be discussed in this presentation
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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".