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Record W2800400630 · doi:10.1139/tcsme-2015-0005

A THEORETICAL AND EXPERIMENTAL STUDY FOR THE LOAD OPTIMIZATION OF GEAR-LIKE PROFILES BY USING FORWARD AND LATERAL EXTRUSION

2015· article· en· W2800400630 on OpenAlexvenueno aff
Tahir Altınbalık, Önder Ayer

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
Fundersnot available
KeywordsExtrusionDie (integrated circuit)Deformation (meteorology)Structural engineeringTrigonometric functionsMechanical engineeringMaterials scienceComputer scienceGeometryMathematicsEngineeringComposite material

Abstract

fetched live from OpenAlex

The main purpose of this research is to investigate the minimum deformation load by selecting a suitable forming method for manufacturing of gear-like sections and to compare the load estimation methods between Upper Bound Analysis and DEFORM-3D. Forward and lateral extrusion were chosen as two different forming methods. The effect of die transition geometry on deformation load was also investigated by straight tapered and cosine profiles. A newly kinematical admissible velocity field to analyze different profiles of extrusion dies was proposed by upper bound analysis. Al 1070 was used as working material. Experiments using five sets of dies with gear-like form were performed, and the measured forming load results were compared with the predictions of the theoretical solutions. Experiments were carried out on the 150 metric ton hydraulic press.

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)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicMetallurgy and Material FormingFrench-language works237,207