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The Microstructure, Creep, and Hardness Properties of Powder Metallurgy Beta Gamma TiAl-4Nb-3Mn Alloy

2012· article· en· W2006325530 on OpenAlexafffund
Trevor Sawatzky, Dongyi Seo, H. Saari, D. Laurin, Young Won Kim

Bibliographic record

VenueMaterials science forum · 2012
Typearticle
Languageen
FieldEngineering
TopicIntermetallics and Advanced Alloy Properties
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMicrostructureLamellar structureCreepTitanium aluminideMetallurgyAlloyPrecipitationHot isostatic pressingPowder metallurgyTitanium alloyComposite materialIntermetallic

Abstract

fetched live from OpenAlex

Pre-alloyed beta gamma titanium aluminide powder with a nominal composition of TiAl-4Nb-3Mn is consolidated by hot isostatic pressing. After consolidation, a step cooled heat treatmentis performed to homogenize the material and produce a fully lamellar microstructure. Various agingheat treatments are then performed with the goal of forming interfacial beta phase precipitates alonglamellar interfaces. The step cooled heat treatment produces a relatively fine microstructure with alamellar spacing of 0.04 μm and an average lamellar colony size of 60 μm. The aging heat treatmentsgenerate beta phase precipitates along lamellar colony boundaries but not along lamellar interfaces,and result in lamellar degredation and grain growth. Constant load tensile creep and room temperaturehardness tests are performed on step cooled heat treated and step cooled heat treated and aged specimens.Creep resistance, generally, improves with aging time, even with no interfacial precipitation,and the lamellar degredation that occurs with aging. The microstructures of the as-tested specimensare characterized and related to the creep properties. The hardness values are also compared as afunction of selected heat treatment condition and microstructural features.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.205
Teacher spread0.192 · 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 teacher head, 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

Citations1
Published2012
Admission routes2
Has abstractyes

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