Nonlinear modeling and bi-objective optimization of inclined screen panel in vibrating flip-flow screen
Bibliographic record
Abstract
The vibrating flip-flow screen (VFFS) is an effective solution for screening sticky and fine materials. To improve screening efficiency, the flip-flow screen panel (FFSP) must periodically tension and relax, generating high vibration intensity (quantified by the g value). However, an excessively high g value will increase the risk of structural damage (related to its stress value). To address the conflicting of maximizing g value and minimizing stress value, this study proposes a nonlinear modeling and multi-objective optimization framework. The effectiveness of nonlinear model and their superiority over linear model have been verified through experiments. Then, a bi-objective particle swarm optimization (PSO) algorithm is employed to simultaneously optimize the structural and excitation parameters of FFSP, which yields a Pareto front. The Pareto front represents the optimal tradeoff between maximizing g value and minimizing stress value with different weights. The results are validated through numerical simulations. This work offers a practical tool for the design of FFSP, with potential applications in dry deep screening technologies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".