MétaCan
Menu
Back to cohort
Record W2747977362 · doi:10.5539/mas.v11n9p51

Investigating of Mechanical Behavior of AA5083 in the Pressing Process Using Finite Element Analysis

2017· article· en· W2747977362 on OpenAlexvenueno aff
Babak Beglarzadeh, Behnam Davoodi

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
Fundersnot available
KeywordsDurabilityMaterials sciencePressingForming processesFinite element methodProcess (computing)AluminiumAlloyStress (linguistics)Die (integrated circuit)LimitingMetallurgyComposite materialStructural engineeringMechanical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

The process of cold forming is considered of the most different industries and the use of such process in the manufacture of components and small parts has expanded. Therefore, analyzing the behavior of metals in this process to identify and control durability that is the main factor of limiting process has particular importance in industrial forming processes. In this study, cold forming process of aluminum metal has been studied and its effect on its mechanical properties has been evaluated. For this purpose, first modeling piece of aluminum alloy 5083 for cold forming process is carried out and using finite element analysis, mechanical properties of considered piece during cold forming processes are investigated. The results show that by reducing friction, stress and strain during the process will reduce, thereby durability of the piece increases, or in other words, ductile fracture occurs in longer life and higher stresses. The results show that by proper forming operations, it can be improved the strength and durability of aluminum alloy. Finally, validation of results, by comparing simulation results with experimental results is carried out.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.050
GPT teacher head0.306
Teacher spread0.256 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicMetallurgy and Material FormingFrench-language works237,207