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Record W2073931676 · doi:10.1115/1.4003962

Wide Gap Braze Repair of Gas Turbine Blades and Vanes—A Review

2011· article· en· W2073931676 on OpenAlexaff
Xiao Huang, Warren Miglietti

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2011
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsCarleton University
Fundersnot available
KeywordsSuperalloyBrazingGas turbinesLimitingOriginal equipment manufacturerTurbineService lifeDamage toleranceService (business)Mechanical engineeringComputer scienceMaterials scienceEngineeringMetallurgyMicrostructureAlloyBusiness

Abstract

fetched live from OpenAlex

Gas turbine blades and vanes in modern gas turbines are subjected to an extremely hostile environment. As such, sophisticated airfoil designs and advanced materials have been developed to meet stringent demands and at the same time, ensure increased performance. Despite the evolution of long-life airfoils, damage still occurs during service thus limiting the useful life of these components. Effective repair of after-service components provides life-cycle cost reduction of engines, and as well, contributes to the preservation of rare elements heavily used in modern superalloys. Among these methods developed in the last four decades for the refurbishment and joining of superalloy components, wide gap brazing (WGB) technology has been increasingly used in the field owing to its ability to repair difficult to weld alloys, to build up substantially damaged areas in one operation, and to provide unlimited compositional choices to enhance the properties of the repaired region. In this paper, the historical development of wide gap repair technology currently used in industry is reviewed. The microstructures and mechanical properties of different WGB joints are compared and discussed. Subsequently, different WGB processes employed at major OEMs are summarized. To conclude this review, future developments in WGB repair of newer generations of superalloys are explored.

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 score0.158
Threshold uncertainty score0.449

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 communication0.0000.000
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 head0.023
GPT teacher head0.251
Teacher spread0.227 · 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

Citations83
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Engineering for Gas Turbines and PowerSame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207