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Record W2732076768 · doi:10.4050/f-0073-2017-12171

Method to Assess the Effects of a Flaw with Residual Stress for Rotorcraft Metallic Structures

2017· article· en· W2732076768 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Diagnostics and Reliability
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsResidual stressStress (linguistics)Materials scienceMetalResidualComposite materialComputer scienceMetallurgyAlgorithm

Abstract

fetched live from OpenAlex

Accounting for effects of damage on the fatigue strength of metallic principal structural elements (PSE) is a requirement for fatigue tolerance evaluation of transport category rotorcraft. Parallel to the current structural test approach for showing regulatory compliance, an analysis method is sought. However, developing and executing an analytical compliance methodology has many challenges. One example is the stress state at the root of a flaw, and how that stress state interferes with the residual stress induced by specific processes such as shot peening. This paper presents an analysis method to determine the stress state at the root of a flaw with the presence of residual stresses. The method can quantify the effect of the residual stress on various flaw shapes and sizes, to avoid overly conservative assumptions in certification analysis.

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)0.000
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 score0.801
Threshold uncertainty score0.299

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 head0.012
GPT teacher head0.279
Teacher spread0.268 · 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

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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