Beta Fleck and Segregation in Titanium Alloy Ingots
Bibliographic record
Abstract
The segregation defect known industrially as "betafleck" occurs in most of the beta and alpha/beta alloys of titanium, arising in segregation during the solidification of the final vacuum arc remelted (VAR) ingot.In this work we use the data generated in Part I of this study to investigate the possible causes of the defect.We conclude that more than one mechanism is potentially operative.In highly alloyed material the defect mechanism is likely to be caused by a similar flow system to that responsible for the "freckle" defect in steels and superalloys: in the lower alloyed materials, it is more likely that the defect arises in the redistribution of equiaxial crystals in the large VAR final ingot liquid pool volume which is solidifying under low temperature gradients.In either case, the industrially-practical solution to the problem appears to lie in VAR melting with steeper temperature gradients at the solidifying interface.We also outline the future need for alloy design to take into account the difference in defect-forming potential of the different betastabilizing elements available for alloy formulation.
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 | 0.000 |
| 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".