MétaCan
Menu
Back to cohort
Record W1196705773 · doi:10.1115/gt2015-42355

Effects of Heat-Treatment Residual Stress on Low Cycle Fatigue Life of a Turbine Disk in PM Superalloy

2015· article· en· W1196705773 on OpenAlexaff
Rongqiao Wang, Da Li, Dianyin Hu, Yang Hai, Jun Song

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsMcGill University
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsSuperalloyResidual stressMaterials scienceTurbineStress (linguistics)ResidualTurbine bladeMetallurgyComposite materialMechanical engineeringMicrostructureComputer scienceEngineering

Abstract

fetched live from OpenAlex

Turbine disks in powder metallurgy (PM) superalloy have been widely used in advanced aeroengines. The production of PM superalloy turbine disks involves a series of heat treatment processes, which would inevitably create residual stresses. It has been proved that the low cycle fatigue (LCF) life of the turbine disk is affected by the residual stresses. The computational simulation of heat treatment is considered as an effective way to evaluate the residual stresses in a turbine disk. A finite element software was used to simulate the heat-treatment processes of a FGH95 turbine disk to obtain the residual stress field. To investigate the relaxation of residual stress in FGH95, smooth bar specimens were measured by X-ray diffraction before and after being loaded. Modified by the residual stresses, SWT model is used to predict the low cycle fatigue life of the turbine disk modified by the residual stress field obtained from the simulation of heat treatment. By the comparison between the prediction modified by the residual stress and the prediction without modification, a considerable decrease in low cycle fatigue life is indicated.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.498

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicHigh Temperature Alloys and CreepFrench-language works237,207