Development of a Novel Magneto-Rheological Elastomer-Based Semi-Active Seat Suspension System
Bibliographic record
Abstract
Human operators in the transportation sector are exposed to whole-body vibration (WBV) while driving. Occupational exposure to WBV, predominant at low frequencies (<20 Hz), has been linked to spinal injuries and reduced functioning. This study aims at the design development of a novel semi-active seat suspension system featuring magneto-rheological elastomers (MREs) to mitigate the WBV. The proposed suspension system allows a greater range of strokes, while ensuring the MRE remains within an acceptable level of deformation. Several MRE samples were fabricated and characterized under shear mode. Afterward, a field- and frequency-dependent phenomenological model was developed to predict the viscoelastic properties of MREs as functions of both the excitation frequency and applied magnetic field. The MRE material model was subsequently used to design and optimize an adaptive seat suspension system incorporating a C-shaped MRE-based isolator in parallel and series with passive springs. The proposed adaptive seat suspension system demonstrated a frequency shift of 29% by increasing the applied current from 0 to 2 A. Finally, a 6-DOF lumped parameter model of a seated human subject combined with the proposed semi-active suspension system featuring the MRE isolator has been formulated to investigate the vibration transmissibility from the floor to the subject’s head.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".