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Record W3036347157 · doi:10.1177/1045389x20930081

Fabrication and characterization of highly controllable magnetorheological material in compression mode

2020· article· en· W3036347157 on OpenAlexaff
Amin Fereidooni, Afonso Martins, Viresh Wickramasinghe, Afzal Suleman

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2020
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of VictoriaNational Research Council Canada
Fundersnot available
KeywordsMagnetorheological fluidMagnetorheological elastomerMaterials scienceElastomerComposite materialSmart materialStiffnessStructural engineeringEngineering

Abstract

fetched live from OpenAlex

This article is focused on the development and characterization of highly controllable magnetorheological materials for stiffness and damping control in semi-active control applications. Two types of magnetorheological materials are developed in-house: magnetorheological elastomer with soft base elastomer, and magnetorheological fluid encapsulated in regular elastomer, namely magnetorheological fluid-elastomer. In both cases of magnetorheological elastomers and magnetorheological fluid-elastomers, the samples are evaluated using in-house-developed shear and compression test rigs, which are equipped with electromagnets and Hall effect sensors for measuring the magnetic field. These features provide the capability to precisely control a wide range of magnetic fields during the experiments. In the case of magnetorheological elastomers, the experimental results of the in-house magnetorheological elastomers are compared with commercially available counterparts made of hard base elastomer. It is shown that the controllability of the material, that is, the relative magnetorheological effect, is significantly improved in the case of magnetorheological elastomer with soft base elastomer. In addition to various magnetic fields, the samples are subjected to a range of loading amplitudes and frequencies. A general trend is observed where the frequency and strain amplitude cause an opposite effect on both the shear and compressive moduli: the increase in frequency gives rise to a higher value of modulus whereas the increase in amplitude reduces the modulus. Furthermore, the effect of bonding on the performance of the magnetorheological elastomers in compression mode is evaluated and the results indicate a significant increase in the modulus and decrease in the loss factor. In all the cases, however, the change of loss factor does not exhibit a predictable trend as a function of magnetic fields. In order to investigate a magnetorheological-based solution for controlling the damping of a semi-active system, magnetorheological fluid-elastomer samples are made in-house. These samples are fabricated using three different iron concentrations, and are tested in compression (squeeze) mode. The results of these experiments confirm that the equivalent damping coefficient of the material rises with the increase in magnetic field, and this effect becomes stronger as the iron concentration of magnetorheological fluids increases. It is also demonstrated that the magnetorheological effect is highly dependent on the loading frequency and amplitude, where the equivalent damping coefficient decreases with the increase in loading frequency and amplitude. In all the aforementioned cases, the stiffness of magnetorheological fluid-elastomers exhibits minor changes, which offers the in-house-developed magnetorheological fluid-elastomers as a damping only control option, a development that is different from the magnetorheological fluid-elastomers reported in the literature.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.209
Teacher spread0.199 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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