Compensation of Magnetic Force of an Electromagnet for Compression Mode Characterization of Magnetorheological Elastomers
Bibliographic record
Abstract
Compression mode characterizations of magnetorheological elastomers (MREs) involve challenges associated with compensating for the magnetic force generated by electromagnets. Different series of magnetic force and flux measurements were performed in order to establish a general systematic methodology for compensating for the magnetic force. In this regard, an optimal design of a UI-shaped electromagnet was realized to facilitate measurements of the force under controlled magnetic flux density up to 1 T. Results revealed notable phase and magnitude differences between the measured static and dynamic magnetic forces, which are found to be mainly dependent on magnetic flux density and frequency. A simple and powerful compensation model was proposed to accurately predict the magnetic force for the entire range of flux density and excitation conditions considered. The proposed model is experimentally validated, and then employed to identify viscoelastic force of an isotropic MRE. Results revealed maximum errors in equivalent stiffness and damping constants of the MRE in the orders of 90% and 163%, respectively, without compensation. The proposed methodology provides a framework for accurate characterization of MREs in the compression mode.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".