MétaCan
Menu
Back to cohort
Record W2515289130 · doi:10.5539/mas.v10n9p232

Precise Positioning of Terfenol-D Actuator by Eliminating the Heat Generated by Coil

2016· article· en· W2515289130 on OpenAlexvenueno aff
M. Ahanpanjeh, Mojtaba Ghodsi, Yousef Hojjat

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTerfenol-DActuatorHeat sinkMaterials scienceElectromagnetic coilMagnetostrictionDisplacement (psychology)Joule heatingThermoelectric coolingWater coolingPositioning systemThermoelectric effectMechanical engineeringOverheating (electricity)AcousticsMagnetic fieldElectrical engineeringComposite materialPhysicsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Large displacement, large generated stress and easy fabrication process lead to the large industrial use of the magnetostrictive materials. However, the thermal expansion restricts their application in precise positioning, which is about 12 and too much to be neglected. In this research, the cooling system was exploited in order to eliminate the joule heat generated by coils under the direct current and solve inaccuracy problem. The investigation consists of two parts; first was design and fabricating the actuator of Terfenol-D with length of 30mm which offers 10 displacement under the DC current of 1A, and second was the cooling system consisting of the thermoelectric cooler, heat sink and control circuit to inhibit the Terfenol-D temperature to be increased. Without the cooling system, as the time was going on and the Terfenol-D was warming up, after about two hours the displacement reached to 68 which deteriorated the accuracy of positioning significantly. By applying the cooling system, the displacement was maintained in 10 with the tolerance of , which realized the precise positioning by the Terfenol-D actuator.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.448

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 studies0.0010.001
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicMagnetic Properties and ApplicationsFrench-language works237,207