Neural network-based decision support for conceptual design of a mechatronic system using mechatronic multi-criteria profile (MMP)
Bibliographic record
Abstract
A mechatronic product is a complex multi-domain system which integrates several disciplines where mechanics are combined with electronics, control and software. The task of designing mechatronic systems is understood to be very tedious and complex because of the high number of components, the multi-physics aspects, the couplings between the different domains involved and the interacting design criteria. Due to this inherent complexity, a systematic and multi-objective approach is needed to replace the traditional methods used to support the design activity and design performance evaluation. In this paper we present a Choquet integral-based neural network alongside with a new multi-criteria profile for mechatronic system performance evaluation in conceptual design stage. The newly introduced Mechatronic Multi-criteria Profile (MMP) includes various quantitative evaluation criteria such as machine intelligence, reliability, complexity, flexibility and cost. The Choquet integral-based neural network will be used for the aggregation of criteria and fitting the intuitive requirements for decision-making in the presence of interacting criteria. Finally, a case study of designing a robotic visual servoing system is presented to validate the effectiveness of the proposed method.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".