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
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Zusammenfassung: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 downtime but also the manufacturing cost. Therefore, there is a strong demand for real-time tool life detection in production lines. In this study, we proposed a stacked LSTM encoder-decoder model for tool wear anomaly detection. Our model only requires “normal” datasets for model training and yields the anomaly score based on the deviation between input and output sequences. This aspect is especially valuable for the actual production lines where the operating conditions are optimized and anomalies are rare. The proposed model is also an end-to-end learning architecture and requires no pre-processing for feature extraction and selection. This characteristic enables real-time processing, which is significant for anomaly detection. In this paper, we first validated our model using artificially generated sinusoidal waveforms and demonstrated its high performance in detecting deviations in amplitude, frequency, waveform bias, and added noise components. The proposed model was then implemented into an actual turning process for Inconel 718 to detect the tool wear in real-time. After being trained by AE (Acoustic Emission) or audio signals captured in the “normal” cutting stage, the model can derive appropriate anomaly scores well matched with the cutting stages of “normal,” “transitional” and “anomalous” corresponding to the tool wear. Finally, deep insight into law/rule-based, data-based, and model-based approaches are discussed.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-023-11497-9