Ata uhi

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

I tiakina i:
I whakaputaina i:Sensors vol. 25, no. 14 (2025), p. 4429-4453
Kaituhi matua: Lalla Abderraouf
Ētahi atu kaituhi: Di Barba Paolo, Hausman Sławomir, Mognaschi Maria Evelina
I whakaputaina:
MDPI AG
Ngā marau:
Simulation
Physics
Deep learning
Magnetic fields
Printed circuit boards
Optimization
Neural networks
Heating
Data processing
Numerical analysis
Design
Geometry
Product development
Efficiency
Urunga tuihono:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Ngā Tūtohu: Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
Whakarāpopotonga: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
Puna:Health & Medical Collection