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

Hosein Tavakoli Hoseini, M. Farahani, M. Sohrabian
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引用次数: 8

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]
基于人工神经网络的625铬镍铁合金电阻点焊工艺分析
研究了重要的电阻点焊工艺参数对625铬镍铁合金焊接接头抗剪抗拉强度的影响。采用全因子设计对电极力、焊接电流、焊接时间等参数进行了试验研究。为了识别各因素的影响及其相互作用,采用了人工神经网络。模型的R2 = 98.11%,证实了ANN模型描述焊接参数与接头强度相关性的有效性。结果表明,焊接电流对接头强度的影响最大,而焊接时间对接头强度的影响最小。焊接参数之间的相互作用只发生在非常高的焊接电流下。结果表明,该人工神经网络模型为625合金的RSW强度表征提供了有益的参考。[2016年10月12日收到;2017年1月23日修订;接受2017年4月26日]
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