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Record W2030423673 · doi:10.5539/ies.v1n1p101

Simulation of Forming Process as an Educational Tool Using Physical Modeling

2008· article· en· W2030423673 on OpenAlexvenueno aff
Azween Abdullah, Mohd Rahimi Muda, Z. Samad

Bibliographic record

VenueInternational Education Studies · 2008
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsPlasticineForgingProcess (computing)Die (integrated circuit)Mechanical engineeringForming processesComputer scienceProcess modelingSheet metalWork (physics)Process simulationMeasure (data warehouse)Manufacturing engineeringPhysical modellingWork in processEngineering drawingEngineeringGeology

Abstract

fetched live from OpenAlex

Metal forming process simulation requires a very high cost including the cost for dies, machine and material and tight process control since the process involve very huge pressure. A physical modeling technique is developed and initiates a new era of educational tool of simulating the process effectively. Several publications and findings have shown a potential of the techniques else where. The objective of the paper is to simulate the closed-die forging. In this work, billet material from plasticine is used and dies are fabricated from perspex. The result indicates that billet volume and geometry of the billet is significant in forming process to measure the filling-ability and forming load required in the process.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.433

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 communication0.0000.001
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 head0.094
GPT teacher head0.412
Teacher spread0.318 · 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 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

Citations3
Published2008
Admission routes1
Has abstractyes

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