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Deep Neural Network-Based Design of Planar Coils for Proximity Sensing Applications

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Pubblicato in:Sensors vol. 25, no. 14 (2025), p. 4429-4453
Autore principale: Lalla Abderraouf
Altri autori: Di Barba Paolo, Hausman Sławomir, Mognaschi Maria Evelina
Pubblicazione:
MDPI AG
Soggetti:
Simulation
Physics
Deep learning
Magnetic fields
Printed circuit boards
Optimization
Neural networks
Heating
Data processing
Numerical analysis
Design
Geometry
Product development
Efficiency
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Abstract:This study develops a deep learning procedure able to identify a planar coil geometry, given the desired magnetic field map. This approach demonstrates its capability to discover suitable coil designs that produce desired field characteristics with high accuracy and efficiency. The generated coils show strong agreement with target magnetic fields, enabling manufacturers to achieve simpler structures and improved performance. This method is suitable for inductive proximity sensors, wireless power transfer systems, and electromagnetic compatibility applications, offering a powerful and flexible tool for advanced planar coil design.
ISSN:1424-8220
DOI:10.3390/s25144429
Fonte:Health & Medical Collection