Data Mining System Architecture for Industrial Internet of Things in Electronics Production

Seidel R, Hassan Amada M, Fuchs J, Thielen N, Schmidt K, Voigt C, Franke J (2020)

Publication Type: Conference contribution

Publication year: 2020

Conference Proceedings Title: 2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME)

Event location: Pitesti

ISBN: 9781728175065

DOI: 10.1109/SIITME50350.2020.9292282

Abstract

Data collection and Machine Learning (ML) have already become reality in industrial applications. Also in electronics manufacturing, some successful approaches for the application of ML have been reported. However, for industrial applications, the infrastructure and the respective analysis-models for such approaches need to cope with the occurring data flow and structured storage in production. This contribution presents a cross-vendor data mining infrastructure setup that allows realtime tracking and structured storage of process data during operation in order to complement the material tracking of manufacturing execution systems (MES). Hence, the developed data mining system is the essential basis for real-time prediction use cases of ML in manufacturing.

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How to cite

APA:

Seidel, R., Hassan Amada, M., Fuchs, J., Thielen, N., Schmidt, K., Voigt, C., & Franke, J. (2020). Data Mining System Architecture for Industrial Internet of Things in Electronics Production. In IEEE (Eds.), 2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME). Pitesti, RO.

MLA:

Seidel, Reinhardt, et al. "Data Mining System Architecture for Industrial Internet of Things in Electronics Production." Proceedings of the 2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME), Pitesti Ed. IEEE, 2020.

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