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Record W4414015796 · doi:10.11159/eee25.103

Advancing Sheet Metal Formability: An In-Depth Numerical Investigation and Enhanced Modeling of Electrohydraulic Process

2025· article· en· W4414015796 on OpenAlexvenueno aff
Ilhem Boutana, Mohamed Rachid Mékidèche, Bachir Benalia, Samir Achouche

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFormabilitySheet metalProcess (computing)Forming processesMaterials scienceMetal formingComputer scienceMetallurgyComposite material

Abstract

fetched live from OpenAlex

Electrohydraulic Forming (EHF) is a cutting-edge manufacturing technique that utilizes high-voltage capacitor discharges within a liquid-filled chamber to efficiently shape sheet metals.By generating a pulsed pressure wave from electrical discharge, EHF achieves rapid plastic deformation, offering significant advantages in precision and versatility.This paper presents an innovative numerical model developed using COMSOL Multiphysics to study the EHF process, incorporating the Johnson-Cook model to simulate plastic deformation.A detailed analysis of formability is conducted for various materials, with a focus on aluminum, copper, and steel, under different operating conditions.Results highlight the significant influence of material properties, electrode-to-workpiece distance, and applied pressure on deformation outcomes.Aluminum emerges as the most promising material for EHF due to its superior formability.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.230
Teacher spread0.223 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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