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An online machine learning framework for early detection of product failures in an Industry 4.0 context

 
: Carvajal Soto, J.A.; Tavakolizadeh, F.; Gyulai, D.

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International journal of computer integrated manufacturing 32 (2019), Nr.4-5, S.452-465
ISSN: 0951-192X
ISSN: 1362-3052
Englisch
Zeitschriftenaufsatz
Fraunhofer FIT ()

Abstract
Current paradigms such as the Internet of Things (IoT) and cyber-physical systems are transforming production environments, where related processes are not only faster and with higher standards, but also more flexible and adaptable to changes in the environment. To address the ever-increasing flexibility requirements while keeping current production standards, a new set of technologies is needed. This paper presents an IoT machine learning and orchestration framework, applied to detection of failures of surface mount devices during production. The paper shows how to build a scalable and flexible system for real-time, online machine learning. Furthermore, the approach is evaluated by using a novel and realistic simulation of a production line for electronic devices as a case study. The system evaluation is done in a holistic manner by analyzing various aspects involving the software architecture, computational scalability, model accuracy, production performance, among others.

: http://publica.fraunhofer.de/dokumente/N-558653.html