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Record W4360894269 · doi:10.1299/jsmemm.2022.os1714

Effect of Strain Rate on Compressive Properties of High-Speed Tool Steel

2022· article· en· W4360894269 on OpenAlexaff
Kohei TATEYAMA, Hiroyuki Yamada, Hidetoshi KOBAYASHI

Bibliographic record

VenueThe Proceedings of the Materials and Mechanics Conference · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsImpact
Fundersnot available
KeywordsMaterials scienceStrain rateSplit-Hopkinson pressure barCompressive strengthComposite materialSofteningStrain (injury)Compression (physics)Bar (unit)Slow strain rate testingFlow stressCompression testDeformation (meteorology)Stress (linguistics)

Abstract

fetched live from OpenAlex

In the present study, the effect of strain rate on compressive properties of high-speed tool steel was experimentally investigated. It is not easy to obtain the stress-strain relationship under high-speed deformation since high-speed tool steel has very high strength. In order to carry out the impact compressive test of high-strength materials, we developed the split Hopkinson bar (SHB) compression test apparatus using cemented carbide for the elastic bar. The impact test was obtained at the strain rate of 103 s-1. The quasi-static test was carried out at strain rates of 10-3 to 10-1 s-1. Both tests were conducted under three temperature conditions, 298 K, 201 K, and 77 K. Within the set of experiments, almost all specimens showed an increase in flow stress with increasing strain rate (strain rate dependence of material strength) was confirmed. However, the impact test at 77 K showed that the effect of work softening was greater than the strain rate dependence of strength.

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)0.002
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 score0.040
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.023
GPT teacher head0.235
Teacher spread0.212 · 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
Published2022
Admission routes1
Has abstractyes

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