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Application of Neural Networks into Prediction of Qualitative Capability of the Preparation Process of Casting Moulds

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the article the problem of assurance of qualitative capability of the preparation process of casting moulds using artificial neural net-works is presented. Using STATISTICA Neural Networks a set of the best networks is found. Obtained results of neural modeling were compared with the results of experimental investigations and classical mathematical modeling. The appropriate architecture of the neural network is chosen that predicts the quality capability of the preparation process of casting moulds with the high precision.
Rocznik
Strony
9--12
Opis fizyczny
Bibliogr. 12 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Manufacturing and Production Engineering, Rzeszow University of Technology, Al. Powstańców War-szawy 8, 35-959 Rzeszów, Poland
autor
  • Department of Manufacturing and Production Engineering, Rzeszow University of Technology, Al. Powstańców War-szawy 8, 35-959 Rzeszów, Poland
  • Department of Materials Forming and Processing, Rzeszow University of Technology, Al. Powstańców Warszawy 8, 35-959 Rzeszów, Poland
Bibliografia
  • [1] Zdanowicz, R. (2003). Automatization of technological processes. Gliwice: WPS (in Polish).
  • [2] Taam, W., Subbaiah, P. & Liddy, J.W. (1993). A note on multivariate capability indices. Journal of Applied Statistics. 20(3), 339-351. DOI: 10.1080/02664769300000035.
  • [3] Raissi, S. (2009). Multivariate process capability indices on the presence of priority for quality characteristics. Journal of Industrial Engineering International 5(9), 27-39.
  • [4] Jaworski, J., Kluz, R. & Trzepieciński, T. (2013). Assurance of quality capability of the preparation process of casting moulds. Archives of Foundry Engineering. 13(12), 61-64.
  • [5] Kluz, R. (2009). Marking the optimum configuration of robotized assembly stand. Archives of Mechanical Technology and Automation. 29(2), 113-122.
  • [6] Kluz R.: „Influence of temperature errors on the assembling precision with the use of the Mitsubishi RV-M2 robot”. Technology and Assembly Automation, 2004, nr 2 (in Polish).
  • [7] Sirvő, M. & Woś, M. (2009). Casting directly from a komputer model by using advanced simulation software FLOW- 3D. Archives of Foundry Engineering. 9(18), 79-82.
  • [8] Ossowski, S. (1996). Sieci neuronowe w ujęciu algorytmicznym. Warszawa: WNT.
  • [9] Chen, H. (1994). A multivariate process capability index over a rectangular solid tolerance zone. Statistica Sinica. 4, 749-758.
  • [10] Shinde, R.L. & Khadse, K.G. (2009). Multivariate process capability using principal component analysis. Quality and Reliability Engineering International. 25, 69-77. DOI: 10.1007/s10182-011-0156-3.
  • [11] Perzyk M., Biernacki R.(2004). Diagnosis of causes of casting defects with use of statistical methods and neural networks. Archives of Foundry, 4(59).
  • [12] Sika, R. Ignaszak, Z.(2011). Data acquisition in modeling using neural networks and decision trees. Archives of Foundry Engineering, 11(23).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-29cfb4f9-f820-4917-a475-21eb2f477a5a
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