Thermal processing strategies enabling boride dissolution and gamma prime precipitation in dissimilar nickel-based superalloys transient liquid phase bond
Bibliographic record
Abstract
The microstructure of dissimilar transient liquid phase bond between Hastelloy X and IN792 nickel-based superalloys is featured by lack of sufficient formation of γ′ precipitates in the bond-centerline and extensive in-situ precipitation of boride second phases in the diffusion affected zones (DAZ). This paper investigates the impact of two thermal processing strategies, using standard solution treatment and aging of IN792 (STA strategy) and using solution treatment of IN792 followed by a post-bond heat treatment utilizing solution treatment of Hastelloy X and aging treatment of IN792 (SPTA strategy) on the joint microstructure and mechanical properties. The boride precipitates in the DAZ of Hastelloy X showed higher resistance to dissolution rather than those in IN792 which can be attributed to the higher content of the boride former elements in Hastelloy X. Both thermal processing routs resulted in the formation of γ′ strengthened bonds with overmatched hardness compared to the Hastelloy X. However, STPA strategy enabled complete dissolution of borides in DAZ of IN792, significant dissolution of borides in DAZ of Hastelloy X, and development of bimodal γ′ distribution in the IN792 base material. Nevertheless, the grain growth in the Hastelloy X after STPA strategy limits the achievable joint shear strength.
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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".