Modeling of Rate-dependent Force-Displacement Behavior of MREs using Neural Networks for Torque Feedback Applications
Bibliographic record
Abstract
Magnetorheological smart elastomers exhibit controllable properties in the presence of controlled external magnetic field. Recently, such elastomers have been used for tactile display applications. In this study, a conceptual design and control framework for utilization of magnetorheological elastomers for torque feedback applications was proposed. As a necessary block in the proposed control framework, a model to obtain the required magnetic field to achieve a desired force-displacement behavior was required. To this end, a neural network-based model was proposed and validated. Also, a search methods based on the nearest neighbor approach was proposed and its performance to predict a desired force profile was assessed with referende to the available experimental data. The proposed neural network was accurate in predicting the force-displacement behavior of three types of MREs (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.97, mean-absolute-error=1.26 N). Also, the proposed search method was successful in obtaining the required magnetic field to demonstrated a desired force-displacement profile (mean-absolute error=3.64 mT). The proposed learning-based MRE model and search method showed favorable performance for incorporation to the proposed torque control framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".