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Record W2799561773 · doi:10.1139/tcsme-2017-0032

Design and development of tractor clutch using combined field and bench tests

2018· article· en· W2799561773 on OpenAlexvenueno aff
Fatih Karpat, Celalettin Yüce, Oğuz Doǧan, Mehmet Onur Genç, Necmettin Kaya

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsClutchTractorDynamometerAutomotive engineeringPowertrainAgricultural machineryTorqueTractive forcePower (physics)Field (mathematics)Rotation (mathematics)EngineeringAgricultureComputer scienceMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Tractors, the primary component of agricultural mechanization, are actively used for maintaining agricultural activities and carrying burdens on agricultural fields under challenging conditions. Tractors are not only required to possess a high tractive force at low operating speeds but also to produce the power necessary for operating the equipment attached to them when used for agricultural purposes. It is of great importance to determine the degree of force that powertrains, especially the clutch, of the tractors are exposed to on agricultural fields and to use the data obtained in this process concerning their guarantee periods. This study measured the motor rotation speed, clutch surface temperature, and frequency and force of pushing the clutch pedal of a tractor used under five different agricultural field conditions. Furthermore, torque and transfer of the dynamometer and power take-off (PTO) were measured. Based on the data obtained from field tests, the tractor clutch development and validation processes were revised. The prototype clutch was produced and tested by completing the required 1 million rotations safely under new boundary conditions within the guarantee period.

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: none
Teacher disagreement score0.685
Threshold uncertainty score0.396

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 head0.019
GPT teacher head0.216
Teacher spread0.196 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

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