Short communication: Part contour error prediction based on LSTM neural network
Abstract Machine tools are subject to multiple sources of error during machining, resulting in deviations in the dimensions of the part and a reduction in contour accuracy. This paper proposes a contour error prediction model based on a long short-term memory (LSTM) neural network, taking hexagonal recess machining as an example and considering the power, vibration, and temperature signals that affect the contour error. The experimental data show that the model can accurately predict the contour error of the machined part. A more accurate and robust contour error prediction model can provide data support for online compensation of contour errors.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Short communication: Part contour error prediction based on LSTM neural network ; volume:14 ; number:1 ; year:2023 ; pages:15-18 ; extent:4
Mechanical sciences ; 14, Heft 1 (2023), 15-18 (gesamt 4)
- Urheber
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Zhang, Yun
Liang, Guangshun
Cao, Cong
Li, Yan
- DOI
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10.5194/ms-14-15-2023
- URN
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urn:nbn:de:101:1-2023011904291876061466
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:34 MESZ
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Beteiligte
- Zhang, Yun
- Liang, Guangshun
- Cao, Cong
- Li, Yan
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