Processing may take a few seconds...

Article

Download file

Title

Evaluating the reliability of groove turning for piston rings in combustion engines with the use of neural networks

Authors

[ 1 ] Instytut Technologii Mechanicznej, Wydział Budowy Maszyn i Zarządzania, Politechnika Poznańska | [ D ] phd student | [ P ] employee

Scientific discipline (Law 2.0)

[2.8] Mechanical engineering

Year of publication

2017

Published in

Archives of Mechanical Technology and Materials

Journal year: 2017 | Journal volume: vol. 37

Article type

scientific article

Publication language

english

Keywords
EN
  • reliability evaluation
  • surface roughness
  • neural networks
Abstract

EN The article describes a method of evaluating the reliability of groove turning for piston rings in combustion engines. Parameters representing the roughness of a machined surface, Ra and Rz, were selected for use in evaluation. At present, evaluation of surface roughness is performed manually by operators and recorded on measurement sheets. The authors studied a method for evaluation of the surface roughness parameters Ra and Rz using multi-layered perceptron with error back-propagation (MLP) and Kohonen neural networks. Many neural network models were developed, and the best of them were chosen on the basis of the effectiveness of measurement evaluation. Experiments were carried out on real data from a production company, obtained from several machine tools. In this way it becomes possible to assess machines in terms of the reliability evaluation of turning.

Pages (from - to)

35 - 40

DOI

10.1515/amtm-2017-0005

URL

https://www.sciendo.com/pl/article/10.1515/amtm-2017-0005

License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Full text of article

Download file

Access level to full text

public

Ministry points / journal

7.0

Ministry points / journal in years 2017-2021

7.0

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.