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Record W2990392922 · doi:10.1115/pvp2019-93312

The Effects of Non-Proportional Biaxial Loading Paths on Ductile Fracture Initiation: A Void Growth Analysis

2019· article· en· W2990392922 on OpenAlexaff
Zhaoyu Jin, Xin Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceVoid (composites)Fracture toughnessComposite materialCoalescence (physics)Finite element methodToughnessPlane stressStructural engineering

Abstract

fetched live from OpenAlex

Abstract The effects of non-proportional biaxial loading paths on ductile fracture initiation toughness are studied in this paper. To this end, the growth of a cylindrical void (hole) located in front of a mode I plane strain crack has been studied using large deformation finite element analysis (FEA). A specific microstructural feature of a steel alloy was thoroughly studied by having a single void positioned at a fixed distance from the crack tip and void that was equal to 10 times the diameter of the void. In particular, the non-proportional biaxial loading path effects on the crack tip blunting, void-growth, ligament reduction and near-tip stress fields are investigated computationally. Under small-scale yielding conditions, one proportional loading and two non-proportional loading paths are applied to the modified boundary layer (MBL) model. It is observed that the non-proportional load paths have a marked effect on the void growth, crack tip blunting and their interaction. By applying the criteria for the coalescence of the crack tip and void, the ductile fracture initiation toughness is estimated. It is shown that the ductile fracture toughness is dependent on loading paths, and the T-stress ratios. Results from this study are of relevance to ductile fracture assessment of components or pressure vessels that operate under non-proportional biaxial loading conditions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.291

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 head0.003
GPT teacher head0.221
Teacher spread0.217 · 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
Published2019
Admission routes1
Has abstractyes

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