@inproceedings{88c6d6ca12f445659b55fd2342a57e5a,
title = "Quality control and fault classification of laser welded hairpins in electrical motors",
abstract = "We present the development, evaluation, and comparison of different neural network architectures using different input data to detect and classify quality deviations in the welding of hairpins. Hairpins are copper rods that are located in the stator of electric motors in electric cars. We use both 3D data and grayscale images as input. The primary challenges are that only a small dataset is available and that high network accuracy is essential to prevent defects in the usage of an electrical engine and to enable a focused rework process. We were able to achieve a 99% accuracy using either 3D data or grayscale images.",
keywords = "Convolutional neural networks, Electric motors, Hairpin, Machine learning, Production, Quality control",
author = "Johannes Vater and Matthias Pollach and Claus Lenz and Daniel Winkle and Alois Knoll",
note = "Publisher Copyright: {\textcopyright} 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.; 28th European Signal Processing Conference, EUSIPCO 2020 ; Conference date: 24-08-2020 Through 28-08-2020",
year = "2021",
month = jan,
day = "24",
doi = "10.23919/Eusipco47968.2020.9287701",
language = "English",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
pages = "1377--1381",
booktitle = "28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings",
}