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Laser Beam Welding of IN792 DS Superalloy

2018· article· en· W2906656460 on OpenAlexaff
G. Barbieri, Francesco Cognini, Vincenzo Bonaiuto, Roberto Montanari, M. Richetta, Alessandra Varone

Bibliographic record

VenueMaterials science forum · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsCentre Casa
Fundersnot available
KeywordsSuperalloyMaterials scienceWeldingMicrostructureMetallurgyHeat-affected zoneElectron beam weldingScanning electron microscopeLaser beam weldingEnergy-dispersive X-ray spectroscopyComposite materialCathode ray

Abstract

fetched live from OpenAlex

The main purpose of the investigation is to develop reliable methods to repair IN 792 superalloy components by Laser Beam Welding (LBW). The jet engine structural components made of nickel-base superalloys are subjected to high stresses and severe environmental, therefore crack may occurs during in-service life. Considering the high cost of this components they are often repaired by welding. Welds must be made: (i) by altering as little as possible the original microstructure; (ii) without introducing in the molten (MZ) and heat affected (HAZ) zones relevant residual stresses; (iii) without producing cracks in MZ and HAZ; (iv) without massive chemical segregation; (v) without elemental diffusion changing the composition of g and g’ phases.LBW has been used to realize seams on 2mm-thick plates of directionally solidified (DS) IN792 superalloy. An Yb fiber laser has been employed in present experiments and the molten pool was shielded from oxidation through a patented equipment. A grid of samples has been prepared by varying the pass speed v from 1 to 2.0 m/min with pre-heating temperature of 200 °C. The microstructural changes occurring in the microstructure of molten zone (MZ) and heat affected zone (HAZ) have been investigated by optical and scanning electron microscopy (SEM) observations and energy dispersion spectroscopy (EDS).

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 categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score0.999

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)0.0020.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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