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Liquation Cracking in Heat Affected Zone in Ni Superalloy Welds

2007· article· en· W2046975338 on OpenAlexafffund
M.C. Chaturvedi

Bibliographic record

VenueMaterials science forum · 2007
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsLiquationSuperalloyMaterials scienceMetallurgyGrain boundaryWeldingIntergranular corrosionInconelCrackingMicrostructureHeat-affected zoneAlloyComposite material

Abstract

fetched live from OpenAlex

Precipitation hardened nickel-based superalloys are widely used in aero and industrial gas turbine engines due to their excellent high temperature strength and remarkable hot corrosion resistance. A drawback of many of these alloys is that they are very difficult to weld due to their high susceptibility to heat affected zone (HAZ) cracking, both during welding and post weld heat treatments (PWHT). Weld cracking in many of these alloys has been attributed mostly to constitutional liquatioin of grain boundary NbC precipitates. however, HAZ cracking has been observed in carbon-free superalloys as well, Therefore, research was initiated to examine grain boundary liquation and cracking in HAZs in a variety of Ni-based superalloys. It was found that intergranular cracking of grain boundaries involved liquation of several other phases, in addition to NbC, that were present in pre-weld microstructure of the alloy. These even included the primary strengthening phase,γ’, in a very widely used superalloy, Inconel 738. In addition, segregation of melting point depressant element B was also observed at gain boundaries in other superalloys, which also caused grain boundaries in HAZ to liquate and resulting in their cracking. An overview of microstructural aspects of different liquation phenomena involved and characteristics of the liquid film contributing to the HAZ microfissuring of nickel superalloys will be discussed in this presentation

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 score0.102
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.220
Teacher spread0.216 · 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

Citations33
Published2007
Admission routes2
Has abstractyes

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