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Record W2006357636 · doi:10.1504/ijmr.2008.016450

An overview on micro-meso manufacturing techniques for micro-heat exchangers for turbine blade cooling

2007· article· en· W2006357636 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.

Bibliographic record

VenueInternational Journal of Manufacturing Research · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTurbine bladeMechanical engineeringHeat exchangerMachiningTurbineBlade (archaeology)Manufacturing engineeringSubtractive colorEngineeringJet engineProcess engineering

Abstract

fetched live from OpenAlex

There is a growing interest in design and manufacturing of micro-heat exchangers embedded in turbine blades of the jet engine for turbine blade cooling. The pertaining manufacturing technologies fall under micro-meso manufacturing, including subtractive methods, additive methods, and near net shaping methods. The purpose of this paper is to provide an overview of micro-meso manufacturing methods under these three categories and discuss their suitability for potential use for mass manufacturing of the turbine blades with integrated micro heat exchangers. Through comparison, several methods are identified as potential candidates, including micro-EDM, micro laser machining, and micro casting.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.643
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.405
Teacher spread0.343 · 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