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

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Abstract

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.

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Acknowledgements

The supports are acknowledged with thanks.

Funding

This work was financially supported by the JST, A-STEP Try-out program (No. 21447369), and METI Monozukuri R&D Support Grant Program for SMEs Grant (No. JPJ005698) in Japan.

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Ibaraki University, Hitachi, 316-8511, Japan

    Taisuke Oshida, Tomohiro Murakoshi, Libo Zhou, Hirotaka Ojima, Kazuki Kaneko, Teppei Onuki & Jun Shimizu

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  1. Taisuke Oshida
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  2. Tomohiro Murakoshi
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  3. Libo Zhou
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  4. Hirotaka Ojima
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  5. Kazuki Kaneko
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  6. Teppei Onuki
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  7. Jun Shimizu
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Contributions

Taisuke Oshida: methodology, software, formal analysis, visualization, investigation, data acquisition, writing—original draft. Tomohiro Murakoshi: methodology, formal analysis, visualization, investigation, data curation, writing—original draft. Libo Zhou: conceptualization, methodology, formal analysis, writing—review & editing, supervision, project administration, funding acquisition. Hirotaka Ojima: conceptualization, formal analysis, writing—review & editing, supervision, funding acquisition. Kazuki Kaneko: visualization, writing—review & editing, supervision. Teppei Onuki: visualization, data curation, writing—review & editing, supervision. Jun Shimizu: visualization, formal analysis, writing—review & editing, supervision.

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Correspondence to Libo Zhou.

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Oshida, T., Murakoshi, T., Zhou, L. et al. Development and implementation of real-time anomaly detection on tool wear based on stacked LSTM encoder-decoder model. Int J Adv Manuf Technol 127, 263–278 (2023). https://doi.org/10.1007/s00170-023-11497-9

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  • DOI: https://doi.org/10.1007/s00170-023-11497-9

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