A New Multi-Criteria Mechatronic Design Methodology Using Niching Genetic Algorithm
Bibliographic record
Abstract
Due to the presence of a wide range of interactive criteria involved in a mechatronic system, a system-based design methodology is needed to achieve optimum mechatronic design. Mechatronic Design Quotient (MDQ) is employed as a multi-criteria design evaluation index in order to develop a concurrent and system-based design approach. MDQ is a multi-criteria index reflecting the global sense of design satisfaction, which is computed by a nonlinear fuzzy integral for aggregation of different criteria. It can be used for the purposes of optimization and/or decision making in different stages of design. In this paper, it serves to evaluate the fitness of design trials in an optimization process. Optimization process is performed in two stages because comprehensive MDQ evaluation of each design trial is time consuming. In the first stage, niching genetic algorithm is used to find local and global optimal design alternatives with respect to some essential MDQ attributes. In the second stage, these local optima will compete with each other, with respect to all criteria involved in MDQ. The developed design methodology offers a concurrent, integrated, and multi-criteria approach, which will provide a mechatronic design that is optimal with respect to the design criteria included in the MDQ. The performance of the developed methodology is validated by applying it to the design of the motion system of an industrial fish cutting machine called Iron Butcher an electromechanical system which falls into the class of mixed or multi-domain.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".