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Record W2135225671 · doi:10.5897/jmer11.063

Experimental studies on the characteristics of AA6082 flow formed tubes

2012· article· en· W2135225671 on OpenAlexvenueno aff
M. Srinivasulu, M. Komaraiah, C. S. Krishna Prasada Rao

Bibliographic record

VenueMechanical Engineering Research · 2012
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMandrelOvalityMaterials scienceRADIUSComposite materialFlow (mathematics)Tube (container)Core (optical fiber)Forming processesDie (integrated circuit)Mechanical engineeringMechanicsEngineering

Abstract

fetched live from OpenAlex

Flow-forming is an innovative, chip less metal forming process used to manufacture thin walled seamless tubes and other axi-symmetric components. Experiments were conducted to form annealed AA6082 alloy tubular pre-forms into thin walled seamless tubes on CNC flow forming machine with a single roller. The process parameters selected for the present investigation are roller radius, mandrel speed, roller feed, thickness reduction, etc. The characteristics of flow formed tube chosen are ovality, mean diameter, thickness variation, surface finish, etc. The effects of these process parameters on the dimensional characteristics and surface quality of flow formed tubes have been studied. The optimum process parameters are proposed to manufacture the tubes with good dimensional characteristics and sound surface quality. It has been found that, roller radius of 4 to 8 mm, thickness reduction ranging from 30 to 35%, mandrel speed of 150 rpm and roller feed in the range of 100 to 130 mm/min formed the tubes with sound characteristics.   Key words: Flow-forming, AA6082 alloy, ovality, mean diameter, surface quality.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.378
Teacher spread0.262 · 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 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

Citations17
Published2012
Admission routes1
Has abstractyes

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