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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-00a78d73-08d3-4276-8cd8-ffd0c08e7408

Czasopismo

Archives of Mechanical Technology and Materials

Tytuł artykułu

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

Autorzy Lisiak, P.  Rojek, I.  Twardowski, P. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
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.
Słowa kluczowe
PL ocena niezawodności   chropowatość powierzchni   sieci neuronowe  
EN reliability evaluation   surface roughness   neural networks  
Wydawca Wydawnictwo Politechniki Poznańskiej
Czasopismo Archives of Mechanical Technology and Materials
Rocznik 2017
Tom Vol. 37
Strony 35--40
Opis fizyczny Bibliogr. 12 poz., fig., tab.
Twórcy
autor Lisiak, P.
  • Poznan University of Technology, Piotrowo 3, 60-965 Poznan, Poland
autor Rojek, I.
  • Kazimierz Wielki University, J. K. Chodkiewicza 30, 85-064 ,Bydgoszcz, Poland
autor Twardowski, P.
Bibliografia
[1] Luo H.Y., Liu J.Y., Wang L.J., Zhong Q.P.: Investigation of the burnishing process with PCD tool on non-ferrous metals, International Journal of Advanced Manufacture Technology 25(2005) 454-459.
[2] Yang Z.-J., Liu J.-Y.: Reliability assessment of burnishing operation of aluminum alloy, Eksploatacja i Niezawodność - Maintenance and Reliability 4(44) (2009) 53-56.
[3] Kuczmaszewski J., Pieśko P.: Wear of milling cutters resulting from high silicon aluminium alloy cast AlSi21CuNi machining, Eksploatacja i Niezawodność - Maintenance and Reliability 16(1) (2014) 37-41.
[4] Siewczyńska M.: Method for determining the parameters of surface roughness by usage of a 3 D scanner, Archives of Civil and Mechanical Engineering 12(1) (2012) 83-89.
[5] Asiltürk I., Çunkas M.: Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method, Expert Systems with Applications 38(2011) 5826-5832.
[6] Karaye D.: Prediction and control of surface roughness in CNC lathe using artificial neural network, Journal of Materials Processing Technology 209(2009) 3125-3137.
[7] Senveter J., Klancnik S., Balic J., Cus F.: Prediction of surface roughness using a feed-forward neural network, Management and Production Engineering Review 1(2) (2010) 47-55.
[8] Zain A.M., Haron H., Sharif S.: Prediction of surface roughness in the end milling machining using artificial neural network, Expert Systems with Applications 37(2010) 1755-1768.
[9] Wojciechowski S., Twardowski P., Pelic M., Maruda R.W., Barrans S., Krolczyk G.: Precision surface characterization for finish cylindrical milling with dynamic tool displacements model, Precision Engineering 46(2016) 146-153.
[10] Przestacki P., Majchrowski R., Marciniak-Podsadna L.: Experimental research of surface roughness and surface texture after laser cladding, Applied Surface Science (2015) http://dx.doi.org/10.1016/j.apsusc.2015.12.093.
[11] Larose D.T.: Discovering Knowledge in Data: An Introduction to Data Mining, John Wiley&Sons, New Jersey, 2005.
[12] Tadeusiewicz R., Chaki R., Chaki N.: Exploring Neural Networks with C#, CRC Press Taylor & Francis Group, Boca Raton, 2014.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-00a78d73-08d3-4276-8cd8-ffd0c08e7408
Identyfikatory
DOI 10.1515/amtm-2017-0005