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Record W1990718067 · doi:10.1115/pvp2012-78092

Constructing a Validated Deformation Mechanisms Map Using Low Temperature Creep Strain Accommodation Processes for Nickel-Base Alloy 718

2012· article· en· W1990718067 on OpenAlexafffund
Mahyar Asadi, Dominic Guillot, Arnaud Weck, Subray R. Hegde, Ashok K. Koul, Trevor Sawatzky, H. Saari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsCarleton UniversityLife Prediction Technologies (Canada)University of Ottawa
FundersUniversity of Ottawa
KeywordsCreepMaterials scienceGrain Boundary SlidingDiffusion creepStrain rateGrain boundaryMetallurgyDislocation creepCavitationDeformation mechanismAlloyMechanicsPhysics

Abstract

fetched live from OpenAlex

A creep Deformation Mechanism Map (DMM) of an engineering alloy can be an effective tool for developing physics-based prognostics systems. Many classical diffusion based rate equations have been developed for time dependent plastic flow where dislocation glide, dislocation glide-plus-climb and vacancy diffusion driven grain boundary migration (diffusion creep) are rate controlling. These creep rate equations have been proven experimentally for simple metals and alloys and form the basis of constructing an Ashby’s DMM. Long term creep testing and analysis of complex engineering alloys has shown that power law breakdown phenomenon is related to the dominance of Grain Boundary Sliding (GBS) as opposed to diffusion creep. Rate equations are now available for GBS in complex alloys and, in this paper, a DMM is constructed for a fine grained Alloy 718 and this is validated by comparison with a collection of experimental data obtained from the literature. The GBS accommodated by wedge type cracking is considered dominant at low homologous temperatures (0.3 to 0.5Tm i. e. melting temperature in Kelvin) whereas GBS accommodated by power-law or cavitation creep dominates above 0.55Tm.

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 score0.005
Threshold uncertainty score0.624

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 studies0.0000.000
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 head0.033
GPT teacher head0.266
Teacher spread0.233 · 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

Citations11
Published2012
Admission routes2
Has abstractyes

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