Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.23919/Eusipco47968.2020.9287701 | 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020) |
Keywords | DocType | ISSN |
machine learning, convolutional neural networks, electric motors, hairpin, quality control, production | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Vater | 1 | 0 | 0.34 |
| Matthias Pollach | 2 | 0 | 0.34 |
| Claus Lenz | 3 | 76 | 7.53 |
| Daniel Winkle | 4 | 0 | 0.34 |
| Alois Knoll Knoll | 5 | 1700 | 271.32 |