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Record W1509230396 · doi:10.4271/2002-01-2568

Optimization of CDCF Grinding of Turbine Blade

2002· article· en· W1509230396 on OpenAlexfundno aff
Changsheng Guo, Terry Green

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
FundersPratt and Whitney Canada
KeywordsBlade (archaeology)GrindingTurbine bladeMechanical engineeringComputer scienceTurbineEngineering

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Continuous-Dress Creep-Feed (CDCF) grinding is used to grind turbine blades made of nickel-based alloys. High specific energy and high forces are the main characteristics of the CDCF process. Due to the complex wheel-workpiece interaction, choosing the right process parameters to increase productivity and reduce cost is often done based on past experience or with a trial-and-error approach. This paper presents a grinding model based approach. The models were found to predict the grinding process behavior well. New process parameters were proposed to reduce cycle time and wheel consumption while maintaining the power and force within the acceptable limits.</div>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.225
Teacher spread0.214 · 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 designObservational
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
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueSAE technical papers on CD-ROM/SAE technical paper seriesSame topicAdvanced machining processes and optimizationFrench-language works237,207