Optimum design parameters and mechanical properties of polymeric nanocomposites using NSGA-II optimization method
Bibliographic record
Abstract
The aim of this work was to develop a method for optimizing both the design parameters and the mechanical properties of polymer-based nanocomposites using multi-objective optimization (MOO) methods. The objective was to maximize both the elastic modulus and the tensile strength of nanocomposites simultaneously by varying the design parameters. The Ji and Zare models were selected as the objective functions for the elastic modulus and tensile strength of polymer nanocomposites, respectively. For this purpose, the NSGA-II approach implemented in MATLAB was used to obtain optimal solutions of the design variables. The optimization model was able to successfully find optimum solutions of the design variables and the overall optimization results were found to be in good agreement with the available published data. In addition, the proposed optimization model was found to be sufficiently accurate in finding the optimum values of the design variables for improving the mechanical properties of nanocomposites.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".