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Record W2969844138 · doi:10.31399/asm.cp.ht2019p0337

Taguchi Design for Heat Treatment of Rene 65 Components

2019· article· en· W2969844138 on OpenAlexaff
Christina Maria Katsari, Stephen Yue, Andrew Wessman

Bibliographic record

VenueProceedings of the ... ASM Heat Treating Society Conference · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsSuperalloyMaterials scienceTaguchi methodsAerospaceMicrostructureMetallurgyVolume fractionTurbine bladeWork (physics)TurbineMechanical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Rene 65 is a nickel-based superalloy used in aerospace components such as turbine blades and disks. The microstructure in the as-received condition of the superalloy consists of about 40% volume fraction of gamma prime precipitates, which is so strong that thermomechanical processing is problematic. The aim of this work is to find a heat treatment to reduce hardness for manufacturing purposes without changing grain size in the final application. For the design of the heat treatments, Taguchi’s L8 matrix is used and the factors that are examined include cooling rate, hold temperature, hold time, and cooling method to room temperature.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.227
Teacher spread0.196 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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