Evaluating the Reliability of Groove Turning for Piston Rings in Combustion Engines with the Use of Neural Networks

Open access

Abstract

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.

References

  • [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.

Journal Information

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 44 44 31
PDF Downloads 9 9 6