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Sensors 2018, 18(9), 3123; https://doi.org/10.3390/s18093123

Sensor-Based Real-Time Detection in Vulcanization Control Using Machine Learning and Pattern Clustering

1
Department of Global Economics, Gachon University, Gyeonggi-do 13120, Korea
2
Department of Data & Knowledge Service Engineering, Dankook University, Gyeonggi-do 16890, Korea
*
Author to whom correspondence should be addressed.
Received: 2 July 2018 / Revised: 1 September 2018 / Accepted: 12 September 2018 / Published: 16 September 2018
(This article belongs to the Special Issue Smart Decision-Making)
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Abstract

Recent paradigm shifts in manufacturing have resulted from the need for a smart manufacturing environment. In this study, we developed a model to detect anomalous signs in advance and embedded it in an existing programmable logic controller system. For this, we investigated the innovation process for smart manufacturing in the domain of synthetic rubber and its vulcanization process, as well as a real-time sensing technology. The results indicate that only analysis of the pattern of input variables can lead to significant results without the generation of target variables through manual testing of chemical properties. We have also made a practical contribution to the realization of a smart manufacturing environment by building cloud-based infrastructure and models for the pre-detection of defects. View Full-Text
Keywords: synthetic rubber compounds; vulcanization process; sensor-based real-time detection model; pattern similarity cluster synthetic rubber compounds; vulcanization process; sensor-based real-time detection model; pattern similarity cluster
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kim, J.; Hwangbo, H. Sensor-Based Real-Time Detection in Vulcanization Control Using Machine Learning and Pattern Clustering. Sensors 2018, 18, 3123.

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