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Record W1561629942 · doi:10.4271/2005-01-0086

Springback Determination By Springback Index Approach

2005· article· en· W1561629942 on OpenAlexaff
Shu-Wen Sun, Hongwei Ma, Zhirui Wang

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndex (typography)Computer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">A new mechanical parameter, Springback Index (SBI), is proposed to reflect the magnitude of sheet-metal springback at a given flow stress level. Through conducting Tension-Unloading-Reloading (TUR) tests and measuring the Effective Unloading Modulus (EUM) Parameter, which is defined as the ratio of the non-linear recovery vs. total recovery, SBI can be determined for the sheet metal after complex forming processes.</div> <div class="htmlview paragraph">To verify this SBI approach, U-free bending tests are conducted with galvanized and galvannealed DP600 sheet steels. The results indicate that the determination of SBI is remarkably simplified and the accuracy of springback prediction is significantly improved.</div>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.967
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.220
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 teacher head, not a consensus.

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
Published2005
Admission routes1
Has abstractyes

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