A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode
Bibliographic record
Abstract
Modeling of highly sophisticated behavior of magnetorheological elastomers (MREs) is an essential step toward optimally designing and effectively controlling the smart material-based devices. While modeling MREs in shear mode has been widely carried out by employing continuum mechanics, mathematical techniques, and phenomenological approaches, the correct determination of dynamic behavior of MREs in tension-compression mode has been addressed in only a few studies due to inherent complexities mainly arising from the computational demandingness of the process. This article addresses the functionality of artificial neural network (ANN) for prediction of MRE's dynamic behavior in tension-compression mode under different levels of strain, frequency, and magnetic flux density. A multilayer perceptron-based feed-forward neural network with backpropagation training technique was used with various structures to identify an optimal configuration. A neural network structure with 20 neurons in the hidden layer was adopted, which revealed the mean square error (MSE) magnitude of 7.1 kPa with R2values above 0.97. Afterward, the predicting capacity of the model was evaluated using experimental data sets. The obtained results are suggestive of reasonably acceptable performance of the proposed ANN model, which holds the capacity for a close mapping of the predicted tension-compression stress values to those of experimental ones. Further development of the proposed ANN model serves as a promising approach to deal with the modeling and controlling of engineering devices equipped with tension-compression MREs.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".