Improved Prediction Model of the Friction Error of CNC Machine Tools Based on the Long Short Term Memory Method Article Swipe
Tao Wang
,
Dailin Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/machines11020243
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/machines11020243
Friction is one of important factors that cause contouring errors, and the friction error is difficult to predict because of its nonlinearity. In this paper, a prediction model of the friction error of a servo system is proposed based on the Long Short-Term Memory method (LSTM). Firstly, the transfer function is used to predict the position of the servo system, and then the prediction error of the transfer function is obtained. Secondly, the nonlinear friction error is extracted and predicted by a LSTM network. Finally, the accurate tracking error can be predicted by the proposed combined model. The experimental results show that the proposed model can improve the prediction accuracy of tracking errors dramatically.
Related Topics
Concepts
Nonlinear system
Servomechanism
Computer science
Tracking error
Control theory (sociology)
Long short term memory
Term (time)
Position (finance)
Mean squared prediction error
Servo
Transfer function
Function (biology)
Tracking (education)
Approximation error
Error detection and correction
Position error
Error function
Algorithm
Artificial intelligence
Artificial neural network
Control engineering
Engineering
Mathematics
Control (management)
Recurrent neural network
Orientation (vector space)
Quantum mechanics
Economics
Biology
Geometry
Physics
Electrical engineering
Evolutionary biology
Psychology
Pedagogy
Finance
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/machines11020243
- https://www.mdpi.com/2075-1702/11/2/243/pdf?version=1675993897
- OA Status
- gold
- Cited By
- 3
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319440461
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4319440461Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/machines11020243Digital Object Identifier
- Title
-
Improved Prediction Model of the Friction Error of CNC Machine Tools Based on the Long Short Term Memory MethodWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-07Full publication date if available
- Authors
-
Tao Wang, Dailin ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/machines11020243Publisher landing page
- PDF URL
-
https://www.mdpi.com/2075-1702/11/2/243/pdf?version=1675993897Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2075-1702/11/2/243/pdf?version=1675993897Direct OA link when available
- Concepts
-
Nonlinear system, Servomechanism, Computer science, Tracking error, Control theory (sociology), Long short term memory, Term (time), Position (finance), Mean squared prediction error, Servo, Transfer function, Function (biology), Tracking (education), Approximation error, Error detection and correction, Position error, Error function, Algorithm, Artificial intelligence, Artificial neural network, Control engineering, Engineering, Mathematics, Control (management), Recurrent neural network, Orientation (vector space), Quantum mechanics, Economics, Biology, Geometry, Physics, Electrical engineering, Evolutionary biology, Psychology, Pedagogy, FinanceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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