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Sequential Optimal Design of Coaxial Magnetic Gears for EV Reducers Using Taguchi Method and Machine Learning Surrogate Model

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Published in:IEEE Access
Format: Online Article RSS Article
Published: 2026
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container_title IEEE Access
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discipline_display Engineering & Technology
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format Online Article
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publishDate 2026
publishDateSort 2026
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spellingShingle Sequential Optimal Design of Coaxial Magnetic Gears for EV Reducers Using Taguchi Method and Machine Learning Surrogate Model
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title Sequential Optimal Design of Coaxial Magnetic Gears for EV Reducers Using Taguchi Method and Machine Learning Surrogate Model
title_auth Sequential Optimal Design of Coaxial Magnetic Gears for EV Reducers Using Taguchi Method and Machine Learning Surrogate Model
title_full Sequential Optimal Design of Coaxial Magnetic Gears for EV Reducers Using Taguchi Method and Machine Learning Surrogate Model
title_fullStr Sequential Optimal Design of Coaxial Magnetic Gears for EV Reducers Using Taguchi Method and Machine Learning Surrogate Model
title_full_unstemmed Sequential Optimal Design of Coaxial Magnetic Gears for EV Reducers Using Taguchi Method and Machine Learning Surrogate Model
title_short Sequential Optimal Design of Coaxial Magnetic Gears for EV Reducers Using Taguchi Method and Machine Learning Surrogate Model
title_sort sequential optimal design of coaxial magnetic gears for ev reducers using taguchi method and machine learning surrogate model
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://ieeexplore.ieee.org/document/11411688