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Record W2883783342 · doi:10.1177/1056789518786031

Strain energy-based multiaxial fatigue life prediction under normal/shear stress interaction

2018· article· en· W2883783342 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 Damage Mechanics · 2018
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsConcordia University
FundersFundamental Research Funds for the Central UniversitiesChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceStructural engineeringTorsion (gastropod)Goodman relationCrackingStrain energyShear stressStrain energy density functionShear (geology)Fatigue testingComposite materialMechanicsStress concentrationFracture mechanicsEngineeringFinite element methodPhysics

Abstract

fetched live from OpenAlex

Through characterizing the interaction of normal/shear stress–strain behavior on material planes of TC4 alloys, a new strain energy critical plane model describing mean stress effects is proposed for life prediction under tension–compression, pure torsion, and tension–torsion loadings. Moreover, a modified Ince–Glinka model is elaborated through considering crack surface close to the maximum shear strain plane. Three simple solutions are presented to determine cracking failure mode using the concepts of life, damage, and strain. Comparing with lifing models of Liu, Smith–Watson–Topper, and modified Ince–Glinka, the proposed model provides more accurate life predictions for TC4 and a compressor turbine disc by full-scale fatigue testing.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.782

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 communication0.0000.001
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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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