Enhanced Multi‐Objective Design Optimisation of Salient Pole Reluctance Magnetic Gear Using Bayesian‐Optimised Artificial Neural Networks
Bibliographic record
Abstract
ABSTRACT The application of artificial intelligence in magnetic gear design has opened new avenues for accelerating computation and optimisation processes. In this paper, a Bayesian‐optimised artificial neural network (ANN) was presented as a surrogate model to predict the performance of salient pole reluctance magnetic gears (SP‐RMGs). The model focuses on key performance indicators such as average torque, torque ripple, and total weight. A diverse dataset generated through Latin hypercube sampling (LHS) is used to train the ANN, which employs customised activation functions to accurately capture the non‐linear characteristics of the magnetic gear. Bayesian optimisation is applied to fine‐tune the hyperparameters, resulting in a significant reduction in computational time. The proposed approach leverages deep learning to efficiently accelerate the multi‐objective optimisation process, providing accurate predictions of SP‐RMG performance metrics. The optimisation results demonstrate significant improvements with the model predicting optimal design parameters that enhance torque performance, reduce torque ripple by 47.2%, and decrease total weight. The proposed approach offers a substantial reduction in computational time while delivering precise optimisation outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".