MétaCan
Menu
Back to cohort

Role of filler alloy composition on laser arc hybrid weldability of nickel-base IN738 superalloy

2013· article· en· W2026887566 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials Science and Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsLiquationMaterials scienceWeldabilityMetallurgySuperalloyHeat-affected zoneWeldingFiller metalIntergranular corrosionPrecipitation hardeningAlloyInconelIndentation hardnessComposite materialArc weldingMicrostructure

Abstract

fetched live from OpenAlex

Laser arc hybrid weldability study was performed on nickel-base IN738 superalloy using nickel-base filler alloys of various compositions. Propensity for heat affected zone (HAZ) intergranular liquation cracking in the weldments was observed to vary depending on the Al+Ti+Nb+Ta concentration of the weld metals produced by the different filler alloys. Detailed study, including SEM and TEM analyses, thermodynamic calculations and microhardness evaluation revealed that variation in HAZ cracking susceptibility of IN738 superalloy when different filler alloys are used during laser arc hybrid welding can be attributed to variation in the extent of precipitation hardening produced in the weld metals. Additionally, shrinkage, and the consequent volumetric changes, due to γ′ precipitation in the weld metals could have contributed to excessive tensile loading of the crack susceptible HAZ and aided intergranular liquation cracking.

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.

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 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 score0.004
Threshold uncertainty score0.281

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 studies0.0000.001
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 head0.200
Teacher spread0.195 · 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