MétaCan
Menu
Back to cohort
Record W4386409825 · doi:10.3390/met13091549

Local Ratcheting at the Notch Region of Non-Press-Fitted and Press-Fitted Al 7075-T6 Samples Undergoing Asymmetric Stress Cycles

2023· article· en· W4386409825 on OpenAlexafffund
Farzad Hatami, A. Varvani‐Farahani

Bibliographic record

VenueMetals · 2023
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceHardening (computing)Structural engineeringComposite materialStress (linguistics)Engineering

Abstract

fetched live from OpenAlex

The present study evaluated the ratcheting response of notched and press-fitted Al 7075-T6 specimens under stress-controlled asymmetric cycles. The degree of the interference fit (DIF) directly influenced the magnitude and the rate of progressive plastic strain at the notch edge region. Local ratcheting at the hole–pin interference region was analyzed by means of two kinematic-hardening rules—the Ahmadzadeh–Varvani (A–V) rule and the Chaboche rule—coupled with the Neuber rule. Ratcheting strains at the notch root of aluminum samples with DIF = 0 (non-press-fitting samples) were measured and found to be the highest in magnitude. For the press-fitted samples, however, ratcheting strains dropped noticeably as the DIF increased from 1% to 2%. The press-fitting process plastically deformed the perimeter edges of the notches and improved the materials strength locally at the notch edges, resulting in better resistance against ratcheting progress. Local ratcheting strains at distances of 0.5, 1.3, and 3.0 mm from the notch roots were predicted for both pinned and unpinned samples via the hardening rules and were compared with those of measured ratcheting values. The ratcheting curves predicted by means of the A-V and Chaboche hardening rules closely agreed with the experimental data. The predicted ratcheting curves were positioned, respectively, above and below the measured ratcheting data.

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 score0.381
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.241
Teacher spread0.220 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueMetalsSame topicHigh Temperature Alloys and CreepFrench-language works237,207