| Neural Network Surrogate-Based Optimization of Electromagnetic Coils for Magnetic Endoscopy |
| کد مقاله : 1453-ISME2026 |
| نویسندگان |
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علی پذیری * دانشجوی دانشگاه صنعتی شریف |
| چکیده مقاله |
| Magnetic actuation offers a minimally invasive approach for active navigation of capsule endoscopic systems, overcoming the limitations of passive wireless motion. This paper presents a surrogate-assisted optimization framework for the design of a triad electromagnetic coil array exhibiting 120° rotational symmetry. The coil geometry, consisting of stepped windings and a ferromagnetic core, is parameterized by ten variables subject to geometric and functional constraints. High‑fidelity 3D magnetostatic FEM simulations are used to generate 6,563 feasible coil configurations within the manufacturing bounds. Independent single‑output multilayer perceptron (MLP) surrogates are trained using an 85/15 data split and z‑score normalization to predict magnetic field magnitude, axial field gradient, and coil mass at a clinically motivated distance of z = 150 mm. The surrogate models achieve high accuracy (R² ≥ 0.92) and replace direct FEM evaluations in a constrained optimization solved using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). Among them, DE provides the most robust convergence and highest objective values. The final optimized coil architecture is validated through full 3D FEM analysis, confirming strong field intensity and symmetry without flux saturation, thereby demonstrating the efficiency and reliability of the proposed NN‑assisted optimization framework. |
| کلیدواژه ها |
| Magnetic Actuation, Electromagnetic Coil Optimization, Surrogate Modeling, Neural Network–Assisted Design, Capsule Endoscopy |
| وضعیت: پذیرفته شده برای ارائه شفاهی |