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Record W4387144401 · doi:10.1115/gt2023-101520

Effect of Heat Treatment on the Morphology of γ′ Precipitates in LW 4275 and LW 4280

2023· article· en· W4387144401 on OpenAlexaff
Ashutosh Jena, Alexandre Gontcharov, Paul Lowden, Mathieu Brochu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsSolvusMaterials scienceSuperalloyMicrostructurePrecipitationVolume fractionScanning electron microscopeMetallurgyAnalytical Chemistry (journal)ChemistryComposite materialChromatography

Abstract

fetched live from OpenAlex

Abstract This study investigates the microstructural evolution of the γ′ precipitates in precipitation hardened Ni-based superalloys LW 4275 and LW 4280 comprising 5.2–5.5 wt. % Al, fabricated by laser powder bed fusion. The as-built microstructure established the presence of discrete carbides, while no signs of γ′ precipitates were present in either LW 4275 or LW 4280 specimens due to a high cooling rate of ∼1e+6 °C/sec. Both specimens were heat treated under a sub-solvus temperature of 1080 °C and super-solvus temperature of 1150 °C, which was established by Thermo-Calc modeling, followed by aging at 705 °C for 24h. Scanning electron microscope (SEM) examinations of samples subjected to sub-solvus heat treatment revealed that the size and volume fraction of the primary γ′ increased while the size and volume fraction of the secondary γ′ decreased as the solutionization period increased from 4 to 24h. Studies of the samples subjected to the super-solvus heat treatment revealed that the volume fraction and size of the γ′ precipitates increased with the increasing solutionization time from 4 h to 24h for both alloys. Finally, a detailed investigation of the morphological evolution of γ′ precipitate after both types of heat treatments was addressed.

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 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 score0.214
Threshold uncertainty score0.164

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)0.0000.000

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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