Application of Neural Networks for Water Meter Body Assembly Process Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13420%2F22%3A43897281" target="_blank" >RIV/44555601:13420/22:43897281 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2076-3417/12/21/11160" target="_blank" >https://www.mdpi.com/2076-3417/12/21/11160</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app122111160" target="_blank" >10.3390/app122111160</a>
Alternative languages
Result language
angličtina
Original language name
Application of Neural Networks for Water Meter Body Assembly Process Optimization
Original language description
The proposed model of the neural network (NN) describes the optimization task of the water meter body assembly process, based on 18 selected production parameters. The aim of this network was to obtain a certain value of radial runout after the assembly. The tolerance field for this parameter is 0.2 mm. The repeatability of this value is difficult to achieve during production. To find the most effective networks, 1000 of their models were made (using various training methods). Ten NN with lowest errors of the output value prediction were chosen for further analysis. During model validation the best network achieved the efficiency of 93%, and the sum of squared residuals (SSR) was 0.007. The example of the prediction of the value of radial runout of machine parts presented in this paper confirms the adopted statement about the usefulness of the presented method for industrial conditions and is based on the analysis of hundreds of thousands of parametric and descriptive data on the process flow collected to create an effective network model.
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
20301 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Sciences
ISSN
2076-3417
e-ISSN
2076-3417
Volume of the periodical
12
Issue of the periodical within the volume
21
Country of publishing house
CH - SWITZERLAND
Number of pages
13
Pages from-to
1-13
UT code for WoS article
000881034400001
EID of the result in the Scopus database
2-s2.0-85141823992