Multiobjective Optimization of Trilayer Polypyrrole Conducting Polymer Actuators
Bibliographic record
Abstract
The main focus of this study is the optimization of a trilayer actuator comprising two layers of polypyrrole and a PVDF membrane core. Since the performance of these actuators is difficult to predict due to their mechanical and chemical properties, optimizing their output behavior such as the tip displacement and blocking force is of crucial importance for utilizing their full potentials and more significantly increasing predictability in their performance. For this purpose, two optimization techniques (multiobjective genetic algorithm and active set algorithm) have been carried out based on a developed mathematical model. Two nonlinear constrained equations representing the tip displacement and the blocking force are formulated and solved for a predetermined thickness of the PVDF core membrane. Both equations are subjected to a bound constraint and a nonlinear equality constraint. The output blocking force and the tip deformation act in a reverse manner and there is a trade-off between them. Accordingly, the results imply that there is no single solution to the problem and a range for each of the design variables should be determined so that there will be a sense of balance between the two objectives. Furthermore, the results obtained from the multiobjective optimization methodology have been verified experimentally.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".