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

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 fo...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-09, Vol.18 (9), p.3123
Main Authors: Kim, Jonghyuk, Hwangbo, Hyunwoo
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial rubber
Big Data
Chemical industry
Clustering
Curing
Electricity distribution
Embedded systems
Emulsion polymerization
Heat
Innovations
Organic chemistry
Pattern analysis
pattern similarity cluster
Polymers
Programmable logic controllers
Raw materials
Real time
Rubber products
sensor-based real-time detection model
Sensors
Sulfur
Synthetic rubber
synthetic rubber compounds
Vulcanization
vulcanization process
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18093123