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Record W3081408834 · doi:10.1139/tcsme-2020-0014

Research on work roll contour for improving the flatness defect of electronic aluminum foil

2020· article· en· W3081408834 on OpenAlexvenueno aff
Haijun Yu, Anrui He, Changke Chen, Xiuliang Wang, Yanjun Liu

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsFlatness (cosmology)FOIL methodSTRIPSContour lineAluminum foilEnhanced Data Rates for GSM EvolutionMaterials scienceAluminiumStructural engineeringEngineeringComposite materialLayer (electronics)PhysicsTelecommunications

Abstract

fetched live from OpenAlex

Electronic aluminum foil strips have extremely large width-to-thickness ratios and often exhibit tight-edge and rib-wave defect after rolling. To improve this special flatness defect, the defect distribution characteristics, section profile of aluminum foil strip, and defect cause were analyzed; and a new work roll contour was designed based on the idea of partial variable diameter. The new work roll contour is a combination of multiple curves, and the influences of the various curve parameters on the contour were researched. A finite element simulation model based on ABAQUS was established to study the control characteristics of the flatness defect when using the new work roll contour. Simulation results and actual production data of the 1450 aluminum foil rolling mill show that the new work roll can increase the depression of the tight-edge area and improve the flatness defect of tight-edge and rib-wave.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicMetallurgy and Material FormingFrench-language works237,207