Development and implementation of real-time anomaly detection on tool wear based on stacked LSTM encoder-decoder model

A severe tool wear is often encountered during the process of turning/milling difficult-to-cut materials like Inconel 718. To protect the cutting process from the tool failure, the countermeasure is currently limited to frequent tool changes. Such preventive solution increases not only the machine d...

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Veröffentlicht in:	International journal of advanced manufacturing technology Jg. 127; H. 1-2; S. 263 - 278
Hauptverfasser:	Oshida, Taisuke, Murakoshi, Tomohiro, Zhou, Libo, Ojima, Hirotaka, Kaneko, Kazuki, Onuki, Teppei, Shimizu, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2023 Springer Nature B.V
Schlagworte:	Acoustic emission Advanced manufacturing technologies Anomalies Assembly lines Audio signals CAE) and Design Coders Computer-Aided Engineering (CAD Cutting wear Datasets Deviation Downtime Encoders-Decoders Engineering Feature extraction Industrial and Production Engineering Internet of Things Mechanical Engineering Media Management Milling (machining) Nickel base alloys Original Article Production costs Production lines Real time Superalloys Support vector machines Tool life Tool wear Turning (machining) Waveforms Unsupervised deep learning Turning process Tool wear Stacked LSTM encoder-decoder model Internet of Things Time sequential signal processing Anomaly detection
ISSN:	0268-3768, 1433-3015
Online-Zugang:	Volltext
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