Development and Characterization of a Novel Hybrid Magnetorheological Elastomer
Bibliographic record
Abstract
Over the past few decades, functional magnetorheological (MR) materials have been extensively researched due to their field-dependent adaptive mechanical properties, which hold substantial promise for implementing semi-active vibration control across various engineering applications. MR elastomers (MRE) are the solid analogue of well-known MR fluids (MRF), where micron-sized ferromagnetic particles are integrated within an elastomeric medium rather than a carrier fluid. In contrast to MRFs, which provide field-dependent variable damping properties, MREs exhibit adjustable stiffness and damping characteristics. MREs also do not experience the sedimentation of magnetic particles and leakage often encountered in MRF-based systems. While there are several studies related to the characterization of MREs operating in shear mode under varying mechanical and magnetic excitation conditions, there are very few studies on the characterization of MREs under compression mode. In particular, the characterization of hybrid MREs, in which MRF is encapsulated within MREs, has been rarely investigated. The objective of the present research is to systematically characterize and compare the viscoelastic properties and dynamic behavior of MREs, MRF-Es (where MR fluid is encapsulated within an elastomeric matrix), and new hybrid MRF-MREs (where MR fluid is encapsulated within MREs), considering the effects of design factors (i.e., shape factor and shape) and mechanical and magnetic loading conditions. To accomplish this, eight different samples of MREs, MRF-Es, and MRF-MREs were fabricated. Dynamic characterization was performed in compression mode under harmonic excitations with varying strain amplitude, frequency, and applied current, ranging from (2.5% to 15%), (0.088 Hz to 10 Hz), and (0 A to 8 A), respectively. Results suggested superior performance of MRF-MREs, exhibiting a relative MR effect of nearly 478%, almost three times that of its counterpart, the MRF-E, and surpassing the MRE by a factor of ten under identical loading conditions. Finally, a phenomenological model was developed based on the modified viscoelastic Kelvin-Voigt model to predict the viscoelastic storage and loss moduli of MREs, MRF-Es, and MRF-MREs as functions of frequency, strain amplitude, and current. The developed model was subsequently used to derive the transmissibility response of an adaptive single-degree-of-freedom (SDOF) system to investigate its capability to tune the natural frequency. An experimental test setup was also designed to confirm the variation in natural frequency of the SDOF system under varying current.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".