{"id":3024,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3024"},"modified":"2022-07-26T13:04:27","modified_gmt":"2022-07-26T11:04:27","slug":"copy-cooling-lubricant-boundary-conditions-for-wet-turning-simulations","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/cutting-technologies-2\/copy-cooling-lubricant-boundary-conditions-for-wet-turning-simulations\/","title":{"rendered":"Multi-stream big data mining for industry 4.0 in machining: novel application of a Gated Recurrent Unit Network"},"content":{"rendered":"\n

by <\/em>Federica Garghetti, Marco Grasso, Massimo Pacella, Giuseppe Fogliazza, Bianca Maria Colosimo (Italy)<\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

In Industry 4.0, the availability of signals from multiple sensors stimulates the investigation of novel quality monitoring and prediction methods. This paper tackles the in-line machining process monitoring by exploiting big data in the shape of multi-stream complex signals, eventually containing degradation and tool wear signatures. The proposed novel solution is fed by real-time multichannel data to identify anomalous states in machining applications. We investigate the effectiveness of a category of ANNs specifically conceived to predict process patterns based on time series of sensor signals, i.e., the Gated-Recurrent-Unit-Network. A real case study shows the efficiency of the proposed solution in predicting wild, complex and drifting patterns, typical of real productions, highlighting its provided benefits for in-line big data mining in industrial applications.<\/p>\n\n\n\n

Keywords<\/strong>: Industry 4.0; Multichannel signals; Recurrent Neural Network; Gated-Recurrent-Unit<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n