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Freckle Formation in Superalloys

2000· article· en· W2127623724 on OpenAlexaff
P. Auburtin, Steve Cockcroft, A. Mitchell, T. Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceSuperalloyMetallurgyMicrostructure

Abstract

fetched live from OpenAlex

The analysis of the formation mechanisms for freckling and for random gram formation in single crystal castings has resulted in a general understanding of the principles involved, but has not been tested extensively against results in actual superalloy castings.In this work, we present the results of experimental work on the production of freckling in superalloy castings, made directionally in a Bridgemau furnace capable of producing DS castings in which the directional axis can be rotated with respect to gravity.The results indicate that we may account quantitatively for freckle formation through an analysis based on fluid-flow as described by a modified Rayleigh criterion.The theoretical critical condition for freckle initiation is found to be a Rayleigh Number of unity; the experimental determination of this critical value is found to be in the region of 0.7 -0.9 depending on the alloy examined.We ascribe the difference in these values to the secondary features of the dendrite morphology.The relation to random grain nucleation is found to lie in the role of isotherm curvature in the solidifying region.This aspect is examined by the use of ProCAST computations in both castings and remelt ingots.morphology may have a large influence on the numerical value of Ra, and therefore on the development of freckling in a casting.Thirdly, the common industrial solution to freckling in directional castings, that of increasing the solidification temperature gradient, is shown to be reasonable in that its primary influence is on the dendrite spacing, but it is also seen to have clear limitations in the case of large thermal sections as would, for example, exist in large castings.Finally, the role of casting geometry and imperfect isotherm control during solidification is not at present part of the analysis and should be included for a full description of the phenomenon.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.230
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2000
Admission routes1
Has abstractyes

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