Bibliographic record
Abstract
Beta fleck is a troublesome segregation defect in many titanium alloys. It has previously been investigated by several authors and appears to have two formation mechanisms, one similar to that of “freckle” in steels and nickel-base alloys, the other arising in the “crystal rain” effect seen in conventional steel ingots. The freckle defect has been extensively studied and several theories developed to account for its formation in both remelted ingots and directional castings. In this work we compare the findings of investigations into the nickel-base freckle formation mechanism to similar conditions in the vacuum arc remelting of titanium alloys. We find that there are strong similarities between the beta fleck formation conditions and the parameters related to the Rayleigh Number criterion for freckle formation. In particular, the dendritic solidification parameters and the density dependence on segregation coefficients both fit well with the conditions proposed to characterise freckle formation. The second formation mechanism arises in the columnar to equiax transition in solidification. The condition for the avoidance of the defect in the two cases is the shown to be the same, namely the use of a very low VAR melting rate, but that it is unlikely to be 100% successful in preventing defect formation. We propose that the techniques presently in use for alloy development in the superalloy field through optimising the composition for minimum sensitivity to freckle formation should be applied to the formulation of future titanium alloys; also that attention should be paid to developing the PAM process to provide suitable solidification conditions for defect absence in a final ingot.
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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 | 0.001 |
| 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 | 0.001 | 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".