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Record W1984208154 · doi:10.1179/174328407x243032

Effect of boron and phosphorus on HAZ microfissuring of Allvac 718 Plus superalloy

2009· article· en· W1984208154 on OpenAlexfundno aff
K.R. Vishwakarma, M.C. Chaturvedi

Bibliographic record

VenueMaterials Science and Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsLiquationMaterials scienceBoronSuperalloyMetallurgyWeldingDuctility (Earth science)AlloyHeat-affected zoneInconelCarbidePhosphorus

Abstract

fetched live from OpenAlex

Heat affected zone (HAZ) microfissuring was investigated in electron beam welds of two Allvac 718 PLUS (718 Plus) alloys with different boron and phosphorus contents. Two preweld solution heat treatments were used and microfissuring susceptibility was evaluated by measuring the HAZ crack lengths in both alloys after bead on plate welding. The segregation behaviour of boron and phosphorus was studied before and after welding by secondary ion mass spectrometry (SIMS). Hot ductility behaviour of the alloys in the standard solution preweld heat treatment was also examined. The results of crack measurements, segregation studies and hot ductility tests correlated well with each other. It was observed that besides constitutional liquation of Nb rich MC type carbides, segregation of B and P largely influenced the microfissuring susceptibility of the alloy. Heat affected zone microfissuring increased with an increase in B + P concentrations and with an increase in the preweld solution heat treatment temperature. Segregation behaviour of B and P and its contribution to microfissuring in Allvac 718 Plus alloy were discussed.

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)0.000
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.003
GPT teacher head0.218
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

Citations38
Published2009
Admission routes1
Has abstractyes

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