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Article


Title

Application of Neural Networks for Water Meter Body Assembly Process Optimization

Authors

[ 1 ] Instytut Technologii Mechanicznej, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ 2 ] Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ 3 ] Instytut Inżynierii Bezpieczeństwa i Jakości, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ P ] employee | [ SzD ] doctoral school student

Scientific discipline (Law 2.0)

[2.8] Mechanical engineering
[5.6] Management and quality studies

Year of publication

2022

Published in

Applied Sciences

Journal year: 2022 | Journal volume: vol. 12 | Journal number: iss. 21

Article type

scientific article

Publication language

english

Keywords
EN
  • neural networks
  • mechanical assembly
  • optimization
Abstract

EN 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.

Date of online publication

03.11.2022

Pages (from - to)

11160-1 - 11160-13

DOI

10.3390/app122111160

URL

https://www.mdpi.com/2076-3417/12/21/11160

Comments

Article Number: 11160

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

100.0

Impact Factor

2.838 [List 2021]

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