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Record W2675857479 · doi:10.1299/jsmedsd.2009.19.163

1309 Optimization of Press Molding Die by Fluid and Structure Coupled Analysis

2009· article· en· W2675857479 on OpenAlexaff
Hiroya FURUICHI, Takashi Sakakibara, Shunsuke Masuda

Bibliographic record

VenueSekkei Kougaku, Shisutemu Bumon Kouenkai kouen rombunshuu/Sekkei Kogaku, Shisutemu Bumon Koenkai koen ronbunshu · 2009
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsDie (integrated circuit)Materials scienceTube (container)Heat transferMolding (decorative)Stress (linguistics)Thermal analysisMaximum temperatureHeat transfer coefficientComposite materialMechanicsThermodynamicsThermalPhysics

Abstract

fetched live from OpenAlex

To prevent quality loss of processed material due to non-uniform temperature and stress of press molding die, a coupled analysis is executed to minimize the difference between maximum and minimum temperature and maximum stress at pressure surface by changing cooling tube location and diameter. The coupled analysis consists of thermo-fluid analysis, heat transfer analysis, and structural analysis, and the continuous calculation required for optimization of the coupled analysis is automated by optimization software. Heat transfer analysis is conducted using temperature distribution of cooling tube which results from thermo-fluid analysis to calculate temperature difference at pressure surface. Structural analysis is conducted using heat strain caused by temperature distribution of die which results from heat transfer analysis to calculate maximum equivalent stress at pressure surface. As a result of optimization after sensitivity analysis, temperature and stress of molding die improved by 76.4% and 34.8%, respectively.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.215
Teacher spread0.209 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2009
Admission routes1
Has abstractyes

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Same venueSekkei Kougaku, Shisutemu Bumon Kouenkai kouen rombunshuu/Sekkei Kogaku, Shisutemu Bumon Koenkai koen ronbunshuSame topicEngineering Applied ResearchFrench-language works237,207