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Record W4367181021 · doi:10.18280/mmep.100232

Experimental and Simulation Study to Reduce Required Bending Force in Air Bending Die by Using Annealing and Rectangular Hole

2023· article· en· W4367181021 on OpenAlexvenueno aff
Maher Ali Hussein, Abbas Allawi Abbas, Mohand Mosa Mohammad

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAnnealing (glass)BendingMaterials scienceSheet metalPlastic bendingStructural engineeringDie (integrated circuit)Composite materialBending stiffnessEngineeringNanotechnology

Abstract

fetched live from OpenAlex

Sheet metal is widely used in industry and in different production processes. The present work aims to reduce the bending force in the air-die bending process. Different mild steel sheet thicknesses (0.5, 0.75, 1, and 1.25 mm) were considered, and two techniques were proposed. The first technique involves an annealing process, while the second one considers cutting a rectangular hole along the bending line. The bending stress was calculated using Autodesk inventor software. The results revealed that the annealing process can provide significant ductility improvement and hence bending force reduction, and that annealing time is an important factor that influences ductility development. Moreover, cutting a hole along the bending line could reduce the bending stress and consequently the bending force. The study also reported that increasing the thickness beyond a specific value could result in a reduction in bending force instead of an increase, which is attributed to a temperature increase in the sheet and bending tool.

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 categoriesMeta-epidemiology (narrow)
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 score0.270
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.046
GPT teacher head0.294
Teacher spread0.248 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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