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2017 №06 (13) DOI of Article
10.15407/as2017.06.14
2017 №06 (15)

Automatic Welding 2017 #06
Avtomaticheskaya Svarka (Automatic Welding), #6, 2017, pp. 87-90
 

Influence of flash butt welding process parameters on strength characteristics of railway rail butts

P.M. Rudenko, V.S. Gavrish, S.I. Kuchuk-Yatsenko, A.V. Didkovsky and E.V. Antipin


E.O. Paton Electric Welding Institute, NASU 11 Kazimir Malevich Str., 03680, Kiev, Ukraine. E-mail: office@paton.kiev.ua
 
 
Abstract
The analysis of basic parameters of flash butt welding was carried out according to the data of current technological reports formed by the computer control system during welding of rails. The opportunity to develop the model for predicting the output quality index of welded butt of a rail, i.e. fracture load of specimen and deflection, was shown based on the parameters of welding process applying different methods of statistical analysis, in particular, correlation and regression analysis and neural networks. The calculations were carried out according to the experimental data obtained at the Kiev rail welding enterprise during welding of rails in the welding machine K1000. 6 Ref., 2 Tables, 5 Figures.
 
Keywords: flash butt welding, statistical models of monitoring and control, monitoring of process parameters, statistical control
 
 
Received:                12.05.17
Published:               06.07.17
 
 
References
  1. Mojseenko, K.V., Mala, T.L. (2016) New welded rails for railways. Specifications. TU U 24.1-40075815-002:2016.
  2. Neural Networks: Manual. www.statsoft.com
  3. Mastitsky, S.E., Shitikov, V.K. (2014) Statistical analysis and visualization of data using R. Moscow: DMK Press.
  4. Dreiper, N., Smith, G. (2007) Applied regression analysis. Moscow: Williams.
  5. Kruglov, V.V., Dli, M.I., Golunov, R.Yu. (2001) Fuzzy logic and artificial neural networks. Moscow: Fizmatlit.
  6. Osovsky, S. (2002) Neural networks for information processing. Moscow: Finances and Statistics.