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.

Identyfikatory

DOI

10.1515/amtm-2017-0005

• 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

EN

reliability evaluation; surface roughness; neural networks

PL

ocena niezawodności; chropowatość powierzchni; sieci neuronowe

• 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.

• Poznan University of Technology, Piotrowo 3, 60-965 Poznan, Poland

Bibliografia

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• [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.
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• [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).