Optimization of neural networks structure selection in modelling spheroidal graphite cast iron for automotive camshafts

Optymalizacja doboru struktury sztucznych sieci neuronowych w modelowaniu zużycia żeliwa sferoidalnego na samochodowe wałki rozrządu

  • Kazimierz Witaszek Silesian University of Technology, Faculty of Transport and Aviation Engineering, Department of Automotive Vehicle Maintenance
  • Krzysztof Garbala AC Spólka Akcyjna
  • Mirosław Witaszek Silesian University of Technology, Faculty of Transport and Aviation Engineering, Department of Automotive Vehicle Maintenance
  • Marcin Rychter Vocational State School of Ignacy Mościcki in Ciechanów, Faulty of Engineering and Economics
Keywords: Artificial neural networks, structure optimization, wear, spheroidal cast iron, Stuttgart Neural Network Simulator, Resilient backPROPagation


The present article discusses the process of optimizing the structure of artificial neural networks applied in modelling the wear of spheroidal graphite cast iron (SG cast iron). The networks were trained using the RPROP gradient method with the application of the SNNS package supported by original self-developed software, which enabled automatic creation, training and testing of networks with different sizes of hidden layers. Based on the results of an analysis of learning process and testing a package of 625 networks, the network was selected which – when modelling the process of spheroidal cast iron wear – generates the slightest errors during testing.


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Eksploatacja i Testy/Exploitation and Tests