Potentials of machine learning in electric drives production using the example of contacting processes and selective magnet assembly

Mayr A, Meyer AW, Seefried J, Weigelt M, Lutz B, Sultani D, Hampl M, Franke J (2017)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2017

Pages Range: 1-8

Conference Proceedings Title: 2017 7th International Electric Drives Production Conference (EDPC)

Event location: Würzburg, Germany

URI: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8328166&isnumber=8328136

DOI: 10.1109/EDPC.2017.8328166

Abstract

Machine learning (ML) is a key technology in data driven industries. In general, ML algorithms offer insight in complex processes by analyzing measured data without acquiring in-depth domain knowledge. In contrast to common physical simulations they do not require excessive computational time and are well suited for real time analysis. This study focuses on transferring the potential of ML to the production of electric drives. Three major issues are identified: the preprocessing of the data, dealing with small data sets and the selection of an appropriate machine learning algorithm. Depending on the specific application in production, different algorithms, for example support vector machines, neural networks, random forests or boosted algorithms come into consideration. The potential of ML in electric drives production is demonstrated using two concrete applications: In the case of contacting technologies, such as thermo and ultrasonic crimping, ML algorithms for predictive maintenance, quality management and process control are considered. The second use case covers the selective magnet assembly. Here, a ML-based concept is proposed that predicts the cogging torque by analyzing magnet properties, as well as process parameters.

Authors with CRIS profile

Andreas Mayr Technische Fakultät Alexander Wilhelm Meyer Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Johannes Seefried Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Michael Weigelt Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Benjamin Lutz Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

How to cite

APA:

Mayr, A., Meyer, A.W., Seefried, J., Weigelt, M., Lutz, B., Sultani, D.,... Franke, J. (2017). Potentials of machine learning in electric drives production using the example of contacting processes and selective magnet assembly. In 2017 7th International Electric Drives Production Conference (EDPC) (pp. 1-8). Würzburg, Germany.

MLA:

Mayr, Andreas, et al. "Potentials of machine learning in electric drives production using the example of contacting processes and selective magnet assembly." Proceedings of the 7th International Electric Drives Production Conference (EDPC), Würzburg, Germany 2017. 1-8.

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