Prediction Analysis of Weld-Bead and Heat Affected Zone in TIG welding using Artificial Neural Networks

International Conference on Recent Advances in Materials & Manufacturing Technologies

Saldanha, Shamith L^1,2 ; Kalaichelvi, V.^1 ; Karthikeyan, R.^2

Department of Electrical and Electronics Engineering, BITS Pilani Dubai Campus, P.O.BOX 345 055, United Arab Emirates^1

Department of Mechanical Engineering, BITS Pilani Dubai Campus, P.O.BOX 345 055, United Arab Emirates^2

关键词: Analytical method;    Back-propagation training algorithms;    Experimental parameters;    Matlab- software;    Performance analysis;    TIG-welding;    Training and testing;    Welding current;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/346/1/012038/pdf
DOI  :  10.1088/1757-899X/346/1/012038

来源: IOP

【 摘 要 】

TIG Welding is a high quality form of welding which is very popular in industries. It is one of the few types of welding that can be used to join dissimilar metals. Here a weld joint is formed between stainless steel and monel alloy. It is desired to have control over the weld geometry of such a joint through the adjustment of experimental parameters which are welding current, wire feed speed, arc length and the shielding gas flow rate. To facilitate the automation of the same, a model of the welding system is needed. However the underlying welding process is complex and non-linear, and analytical methods are impractical for industrial use. Therefore artificial neural networks (ANN) are explored for developing the model, as they are well-suited for modelling non-linear multi-variate data. Feed-forward neural networks with backpropagation training algorithm are used, and the data for training the ANN taken from experimental work. There are four outputs corresponding to the weld geometry. Different training and testing phases were carried out using MATLAB software and ANN approximates the given data with minimum amount of error.