MétaCan
Menu
Back to cohort
Record W2022483811 · doi:10.1520/jai12037

Notch-Root Elastic-Plastic Strain-Stress in Particulate Metal Matrix Composites Subjected to General Loading Conditions

2005· article· en· W2022483811 on OpenAlexaff
GM Owolabi, MNK Singh

Bibliographic record

VenueJournal of ASTM International · 2005
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials sciencePlasticityComposite materialLinear elasticityStress (linguistics)Matrix (chemical analysis)Finite element methodStress–strain curveStructural engineeringDeformation (meteorology)Engineering

Abstract

fetched live from OpenAlex

Abstract Determining the stress and strain history at the point of highest stress concentration in particulate metal matrix composites (PMMCs) is complicated, particularly when they have a finite concentration of inclusions, the matrix material in the vicinity of the notch is elastic-plastic, and when multiaxial cyclic loads are applied to the component. In this paper, an analytical tool is developed to approximate notch root elastic-plastic strains and stresses in PMMC components subjected to multiaxial cyclic loads. The model consists of a set of linear relations that can be solved to estimate a notch root elastic-plastic strain and stress history in PMMCs from an elastic analysis. The model is developed using assumptions about notch root behavior, the incremental mean field theory, and the endochronic theory of plasticity. The model presented provides an easy to implement approximation to the otherwise rather complex non-linear problem. The analytical results are compared to the local strains, obtained using 3D image correlation technology, at the depth of a circumferential notch in a PMMC bar subjected to proportional and non-proportionally applied monotonic and cyclic axial-torsional loads. The results of the comparison show that the proposed model works well for the geometry and load paths considered.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.513

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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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 designSimulation or modeling
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

Citations1
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of ASTM InternationalSame topicFatigue and fracture mechanicsFrench-language works237,207