@inproceedings{5d0cb5a1066341cfbdd3618e36029d7f,
title = "A Machine Learning Based Method to Efficiently Analyze the Cogging Torque under Manufacturing Tolerances",
abstract = "This paper addresses a new technique based on machine learning which reduces the number of evaluations required to perform robustness analysis of permanent magnet synchronous machines. This methodology is based on the logical behavior of possible faulty magnet combinations produced by manufacturing tolerances. Groups of faulty combinations with a similar structure and cogging output are identified by means of a fuzzy-logic algorithm. Subsequently, only a single faulty combination of each group needs to be evaluated through the finite element method, which severely decreases the computational burden of the tolerance analysis. A 6-slot 4-pole and a 9-slot 6-pole machine were subject to tolerance analysis considering the displacement of the magnets. Both machines were evaluated through the proposed method and the results were validated by means of the finite element method (FEM).",
keywords = "Fuzzy logic, machine learning, permanent magnet, robustness, tolerance analysis",
author = "Andrea Reales and Werner Jara and Gabriel Hermosilla and Carlos Madariaga and Juan Tapia and Gerd Bramerdorfer",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 ; Conference date: 10-10-2021 Through 14-10-2021",
year = "2021",
doi = "10.1109/ECCE47101.2021.9595571",
language = "English",
series = "2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1353--1357",
booktitle = "2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings",
}
