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Record W3156772034 · doi:10.5267/j.esm.2021.1.005

Evaluation of the sensitivity of springback to various process parameters of aluminum alloy sheet with different heat treatment conditions

2021· article· en· W3156772034 on OpenAlexvenueno aff
S. L. Hakimi, A. Soualem

Bibliographic record

VenueEngineering Solid Mechanics · 2021
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSheet metalMaterials scienceForming processesBendingWork (physics)Boundary value problemAlloySensitivity (control systems)Work hardeningHardening (computing)Material propertiesUltimate tensile strengthMechanical engineeringStructural engineeringDeformation (meteorology)Computer scienceMetallurgyComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

The forming steps by permanent deformation controlled by the tools generate a distribution of stresses inside the material which directly depends on the work hardening properties of the latter. The change in boundary conditions following the removal of the tools imposes the material to redistribute the stresses in the sections in a manner compatible with the new boundary conditions. This new distribution necessarily operates by local elastic deformations that result globally in a general change of shape called springback. This geometrical deviation can be minimized by the meticulous focus of the tools, but it cannot generally be completely annihilated due to the influence of several parameters. For this reason, the study of the influence of the different technological factors and physico-metallurgical parameters on the springback for the different metals is very important to design and properly realize forming tools. The main objective of this work is to find solutions to problems encountered in sheet metal forming such as the problem of springback. Our work has two essential purposes: the first is summarized in an experimental study based on theoretical analyses. To this end, much effort is made to add a new design of parts for a U-type stretch-bending device and adapt it to a tensile testing machine. This design has the advantage of modifying and assembling all parameters affecting springback at the same time and also of carrying out several forming processes on the same device. The second goal is the experimental and numerical prediction of springback, and the study of the effect of various stretch-bending process parameters such as punch velocity, the orientation of the sheet (anisotropy), hold time and punch-die clearance on springback behavior under heat treatment of aluminum alloy sheets with three different rolling directions (0°,45°,90°). A finite element (FE) model of stretch-bending has been established by utilizing ABAQUS/CAE software. From this analysis, it can be concluded that the springback is affected by the anisotropy of the sheet and the heat treatment in the stretch-bending process. The obtained experimental results were compared with the numerical simulations found in good agreement.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.553

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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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