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Effect of composition and microstructure on the fatigue and creep-fatigue behaviour of Allvac 718Plus alloy

2010· article· en· W2001831517 on OpenAlexaff
J. Tsang, Richard Kearsey, P. Au, D.Y. Seo, Scott Oppenheimer, W. Cao

Bibliographic record

VenueMaterials at High Temperatures · 2010
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceSuperalloyMicrostructureMetallurgyCreepAlloyInconelGrain size

Abstract

fetched live from OpenAlex

ATI Allvac has recently developed Allvac® 718Plus™ (718Plus), a new class of nickel-base superalloy to be used in high temperature turbine engine applications. This new alloy has an in-service temperature capability that is 55°C (100°F) higher than Inconel 718 and has better processing characteristics, thermal stability, and lower raw material cost compared with other superalloys such as Waspaloy. The fatigue and creep-fatigue behaviour of three different microstructural conditions of 718Plus were investigated; the standard fine grain (ASTM 8 ±1) microstructure produced by the standard heat treatment, the standard fine grain microstructure after long term thermal exposure (732°C for 1000 h), and a fine grain microstructure with modified δ-phase particles.All three conditions of 718Plus showed a significantly improved threshold for fatigue crack propagation over the widely used disc material, Waspaloy. This is an important material property for damage tolerant engine design as it indicates that 718Plus has a potentially higher application stress range for which cracks or defects are effectively non-propagating. Waspaloy showed better creep-fatigue crack growth resistance than the 718Plus materials but prolonged thermal exposure does improve this property considerably, making it comparable to Waspaloy.

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)0.000
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 score0.029
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

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