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Record W2800568084 · doi:10.1139/tcsme-2006-0015

ELASTIC MODULUS ADJUSTMENT PROCEDURES (EMAP) IN METAL FORMING ANALYSIS

2006· article· en· W2800568084 on OpenAlexaffvenue
R. Adibi-Asl, R. Seshadri

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFinite element methodLimit loadLimit (mathematics)PlasticityLimit analysisElastic modulusModulusStructural engineeringMechanicsMaterials scienceMathematicsMathematical analysisEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

The estimation of exact loads or forces that cause plastic flow of the material in a metal forming process is often difficult. The main objective of this paper is to estimate the limit loads for some well-known metal forming processes, using a new generation of robust simplified methods. These methods, based on iterative linear elastic finite element analyses, are implemented by modifying the local elastic modulus of the material during subsequent iterations. On account of the possibility of local plastic collapse, the reference volume concept is invoked in order to identify the kinematically active and dead zones in the metal component. The reference volume method is shown to give a reasonable prediction of the limit load. The estimates of limit loads are then compared with corresponding results obtained using inelastic finite element analysis, and analytical solutions, with good effect.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.202
Teacher spread0.196 · 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
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
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicMetal Forming Simulation TechniquesFrench-language works237,207