Applying the MIMO BP Neural Network and Cloud-Based Monitoring of Thermal Behavior for High-Speed Motorized Spindle Units
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10254748" target="_blank" >RIV/61989100:27230/24:10254748 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001192996500001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001192996500001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/machines12030194" target="_blank" >10.3390/machines12030194</a>
Alternative languages
Result language
angličtina
Original language name
Applying the MIMO BP Neural Network and Cloud-Based Monitoring of Thermal Behavior for High-Speed Motorized Spindle Units
Original language description
Understanding the temperature-working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20300 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machines
ISSN
2075-1702
e-ISSN
2075-1702
Volume of the periodical
12
Issue of the periodical within the volume
3
Country of publishing house
CH - SWITZERLAND
Number of pages
21
Pages from-to
"MAR 2024"
UT code for WoS article
001192996500001
EID of the result in the Scopus database
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