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Record W2964190447 · doi:10.9773/sosei.60.189

Development and Application of High-Performance Frictional Gas Cushion Device

2019· article· en· W2964190447 on OpenAlexaff
Takao Ito, Takashi SHIMOMA, Hiroaki Ishikawa, Osamu Takai

Bibliographic record

VenueJournal of the Japan Society for Technology of Plasticity · 2019
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsCushioningCushionPiston (optics)Materials scienceCylinderMechanicsMechanical engineeringComposite materialStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Recent ultrahigh-tensile-strength steel sheets have become indispensable for improving fuel economy through their incorporation into skeletal parts of automobiles to reduce the weight. To enable the forming of ultrahigh-tensile-strength steel sheets, a high-performance upper cushion is indispensable. The developed high-performance frictional gas cushion device applies as cushion force by frictional force generated by sliding between metal parts of the shaft parts and the hole parts having interference. Frictional cushion force mechanism was added to the upper piston shaft portion of the gas cushion device. In this apparatus, in addition to the repulsive force (gas cushion force) generated by the gas when the piston is pushed into the cylinder, frictional force (frictional cushioning force) due to interference between the inner surface of the cylinder and the outer surface of the piston shaft can be obtained. Therefore, it is possible to obtain a higher cushioning force than that with the conventional gas cushioning apparatus, which generates cushioning force only by the repulsive force of the gas.

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 score0.144
Threshold uncertainty score0.232

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Explore more

Same venueJournal of the Japan Society for Technology of PlasticitySame topicMetal Forming Simulation TechniquesFrench-language works237,207