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• W2890790289 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." @default.
• W2890790289 created "2018-09-27" @default.
• W2890790289 creator A5022690640 @default.
• W2890790289 creator A5047800601 @default.
• W2890790289 date "2018-09-16" @default.
• W2890790289 modified "2023-10-06" @default.
• W2890790289 title "Sensor-Based Real-Time Detection in Vulcanization Control Using Machine Learning and Pattern Clustering" @default.
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• W2890790289 doi "https://doi.org/10.3390/s18093123" @default.
• W2890790289 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6163192" @default.
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