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Validation of Dynamic Hardening Models with Taylor Impact Tests at High Strain Rates

2014· article· en· W1995190530 on OpenAlexaff
Ming Jun Piao, Chung Hee Park, Hoon Huh, Ik Jin Lee

Bibliographic record

VenueKey engineering materials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMaterials scienceStrain hardening exponentHardening (computing)Strain rateComposite materialReliability (semiconductor)Structural engineeringEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

This paper is concerned with the hardening behavior of 4340 steel at high strain rates from 104s-1to 106s-1. Tension tests were conducted using Instron 5583, HSMTM and SHPB testing machines at a wide range of strain rate from 10-3s-1to 103s-1. Three different impact velocities were performed for the Taylor impact tests to evaluate the reliability of Johnson–Cook model, modified Johnson–Cook model, modified Khan–Huang model, and Lim–Huh model at high strain rates for 4340 steel.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.248
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations1
Published2014
Admission routes1
Has abstractyes

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