Title: Process analysis of resistance spot welding on the Inconel alloy 625 using artificial neural networks

Authors: Hosein Tavakoli Hoseini; Mohammadreza Farahani; Majid Sohrabian

Addresses: School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran ' School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran ' Faculty of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract: In this article, the influence of the important resistance spot welding process parameters on the shear-tensile strength of the welded joints of Inconel alloy 625 was investigated. Experimental study using full factorial design of the electrode force, welding current, welding time parameters was conducted. In order to identify the effects of each factor and their interaction, the artificial neural network was employed. The R² equal to 98.11% of the model confirmed the effectiveness of the ANN model for describing the correlation between the welding parameters and joint strength. It was observed that the welding current was the most influential process parameter on the joint strength and in return the welding time had the least influences. Interaction between the welding parameters occurred only at very high welding currents. It was observed that the ANN model provides a lucrative reference for RSW strength characterisation of Inconel alloy 625. [Received 12 October 2016; Revised 23 January 2017; Accepted 26 April 2017]

Keywords: Inconel alloy 625; artificial neural network; ANN; resistance spot welding; RSW; shear tensile strength.

DOI: 10.1504/IJMR.2017.088398

International Journal of Manufacturing Research, 2017 Vol.12 No.4, pp.444 - 460

Received: 28 Sep 2016
Accepted: 26 Apr 2017
Published online: 05 Dec 2017 *

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