[Kovove materialy - Metallic materials]
    Mon - January 25, 2021 - 14:43 No. of hits : 1227830 ISSN 1338-4252 (online) ISSN 0023-432X (printed)
© Institute of Materials and Machine Mechanics, Slovak Academy of Sciences, Bratislava, Slovak Republic

VOLUME 57 (2019), Issue 3

Artificial Neural Network application to the friction welding of AISI 316 and Ck 45 steels
ERSOZLU, I., CELIK, S.
vol. 57 (2019), no. 3, pp. 199 - 205

Abstract
The optimization of the friction welding parameters through experimental studies does not only cause loss of time and materials but also increases the cost. In this study, an Artificial Neural Network (ANN) model is developed for the analysis of the correlation between the friction welding parameters and tensile strength of both AISI 316 austenitic-stainless steel and Ck 45 steel. The input parameters of the model are friction time, friction pressure and upset pressure while tensile strength is the output. Experimental data are used to train and test the neural network. A good correlation was obtained between the experimental values and the ANN model prediction (R2 = 0.9711). By using this model, the number of experiments to obtain optimal parameters of friction welding and number of tensile tests could be minimized.

Key words
friction welding, AISI 316 stainless steel, Ck 45 steel, mechanical properties, Artificial Neural Network (ANN)

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Full title of this journal is bilingual: Kovové materiály - Metallic Materials.
The official abbreviation in accordance with JCR ISI is Kovove Mater.


Article abstracts updated: 2021-01-15 08:18      
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