Hard–Soft–Hard Magnetic Laminated Magnetorheological Elastomers: Paving the Way to High Bidirectional Modulus Regulation and Superior Mechanical Properties
Bibliographic record
Abstract
With the increasing demand for vibration isolation in advanced manufacturing, precision instruments must not exhibit significant vibrations under broadband excitation, necessitating real-time stiffness adjustment in vibration isolation systems to effectively isolate external low and high-frequency vibrations. Magnetorheological elastomer (MRE) semiactive vibration isolators can adapt well to broadband environments, but current bidirectional modulus regulation methods are either overly complex or ineffective. Modifying the internal MRE is the most direct and effective approach. Achieving pronounced bidirectional modulus regulation while maintaining mechanical properties and MR effects in MREs is a current research challenge. This study creatively used a stratified curing method to fabricate an anisotropic laminated MRE (L-MRE) with hard magnetic MRE (H-MRE) on both ends and conventional MRE in the middle layer. The SEM, EDS, XRM, and optical imaging confirmed the successful fabrication of L-MRE. Theoretical analysis and testing validated that L-MRE seamlessly integrates the magnetic and mechanical advantages of H-MRE and MRE, showcasing great advantages of the complementary effect of composite materials. Most importantly, compared to traditional MRE materials, the lamination effect endows L-MRE with satisfactory bidirectional modulus regulation. This research provides an innovative material with reference value for the application demands of broadband vibration reduction technology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".