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  1. 学術雑誌掲載済論文

Developing sensor signal-based digital twins for intelligent machine tools

https://kitami-it.repo.nii.ac.jp/records/2000347
https://kitami-it.repo.nii.ac.jp/records/2000347
06375c75-804e-4c08-b7a0-e8c65590b2d2
名前 / ファイル ライセンス アクション
1-s2.0-S2452414X21000418-main 1-s2.0-S2452414X21000418-main (2).pdf (7.5 MB)
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Item type 学術雑誌論文 / Journal Article(1)
公開日 2022-12-15
タイトル
言語 en
タイトル Developing sensor signal-based digital twins for intelligent machine tools
言語
言語 eng
資源タイプ
資源 http://purl.org/coar/resource_type/c_6501
タイプ journal article
著者 Angkush Kumar Ghosh

× Angkush Kumar Ghosh

en Angkush Kumar Ghosh

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AMM Sharif Ullah

× AMM Sharif Ullah

en AMM Sharif Ullah

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Roberto Teti

× Roberto Teti

en Roberto Teti

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Akihiko Kubo

× Akihiko Kubo

en Akihiko Kubo

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抄録
内容記述タイプ Abstract
内容記述 Digital twins can assist machine tools in performing their monitoring and troubleshooting tasks autonomously
from the context of smart manufacturing. For this, a special type of twin denoted as sensor signal-based twin
must be constructed and adapted into the cyber-physical systems. The twin must (1) machine-learn the required
knowledge from the historical sensor signal datasets, (2) seamlessly interact with the real-time sensor signals, (3)
handle the semantically annotated datasets stored in clouds, and (4) accommodate the data transmission delay.
The development of such twins has not yet been studied in detail. This study fills this gap by addressing sensor
signal-based digital twin development for intelligent machine tools. Two computerized systems denoted as
Digital Twin Construction System (DTCS) and Digital Twin Adaptation System (DTAS) are proposed to construct
and adapt the twin, respectively. The modular architectures of the proposed DTCS and DTAS are presented in
detail. The real-time responses and delay-related computational arrangements are also elucidated for both systems.
The systems are also developed using a Java™-based platform. Milling torque signals are used as an
example to demonstrate the efficacy of DTCS and DTAS. This study thus contributes toward the advancement of
intelligent machine tools from the context of smart manufacturing.
言語 en
書誌情報 en : Journal of Industrial Information Integration

巻 24, p. 100242, 発行日 2021
出版者
言語 en
出版者 Elsevier
著者版フラグ
言語 en
値 publisher
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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