Volume 2, Issue 2 (Journal OF Welding Science and Technology of Iran 2016)                   2016, 2(2): 47-55 | Back to browse issues page

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Mollapour Y, Aghakhani M, Eskandari H, Azarioun2 H. Modeling of weld penetration in SAW process in the presence of boehmite nano-particles surface adsorbed by boric acid using MLP-ANN. Journal title 2016; 2 (2) :47-55
URL: http://jwsti.iut.ac.ir/article-1-97-en.html
Abstract:   (11100 Views)

This paper investigates the effect of boehmite nano-particles surface adsorbed byboric acid (BNBA) along with other input welding parameters such as welding current, arc voltage, welding speed, nozzle-to-plate distance on weld penetration. Weld penetration modeling was carried out using multi-layer perceptron artificial neural network (MPANN) technique. For the sake of training the network, 70% of the obtained data from experimentation using five-level five-factor central composite rotatable design of experiments was used. The performance of the network shows a good agreement between the experimental data and the data obtained from the network. Hence, it is to be concluded that MPANN is highly accurate in predicting the weld penetration in SAW process.

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Type of Study: Research | Subject: Special

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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