Optimization of Mechatronic Design Quotient Using Genetic Algorithm in Vibration Controllers for Flexible Beams
Bibliographic record
Abstract
Due to their extensive utilization in engineering designs, various vibration controllers have been investigated with design specifications in mind. The optimization of vibration controller designs is a complicated multi-criteria problem. In this paper, the mechatronic design quotient (MDQ) approach and genetic algorithm (GA) are coupled together to determine this optimal design solution. The MDQ is presented to formulate an evaluation function for the passive vibration controller design of flexible beam structures, and the GA is then used to maximize this function so as to achieve a design solution with the highest MDQ value. For comparison, both the MDQ performance of passive vibration controller design using linear dampers and active vibration controller design using a linear quadratic regulator are provided. The latter is used as the performance benchmark to evaluate the optimal design solution of the former. Experimental results show that the linear dampers design with the proposed method can achieve similar performance to an optimal active controller.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".