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

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 fo...

Full description

Saved in:
Bibliographic Details
Published in2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME) pp. 75 - 80
Main Authors Seidel, Reinhardt, Amada, Hassan, Fuchs, Jonathan, Thielen, Nils, Schmidt, Konstantin, Voigt, Christian, Franke, Jorg
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.10.2020
Subjects
Data analytics
Data mining
Electronics manufacturing
Machine learning
Manufacturing
Production
Protocols
Servers
Smart electronic module assembly
Smart electronics manufacturing
Sockets
TCPIP
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2642-7036
DOI:10.1109/SIITME50350.2020.9292282