Identification of key GMAW fillet weld parameters and interactions using artificial neural networks

Cairns, J.W.P. and McPherson, N.A. and Galloway, A.M. (2015) Identification of key GMAW fillet weld parameters and interactions using artificial neural networks. Welding and Cutting, 15 (1). pp. 51-57. ISSN 1612-3433 (In Press)

[thumbnail of Cairns-Mcpherson-Galloway-WC2016-identification-of-key-gmaw-fillet-weld-parameters]
Preview
 Text. Filename: Cairns_Mcpherson_Galloway_WC2016_identification_of_key_gmaw_fillet_weld_parameters.pdf
Accepted Author Manuscript
Download (946kB)| Preview

Abstract

Fillet welds are one of the most commonly used weld joints but one of the most difficult to weld consistently. This paper presents a technique using Artificial Neural Networks (ANN) to identify the key Gas Metal Arc Welding (GMAW) fillet weld parameters and interactions that impact on the resultant geometry, when using a metal cored wire. The input parameters to the model were current, voltage, travel speed; gun angle and travel angle and the outputs of the model were penetration and leg length. The model was in good agreement with experimental data collected and the subsequent sensitivity analysis showed that current was the most influential parameter in determining penetration and that travel speed, followed closely by current and voltage were most influential in determining the leg length. The paper also concludes that a ‘pushing’ travel angle is preferred when trying to control the resultant geometry mainly because both the resultant leg length and penetration appear to be less sensitive to changes in heat input.

ORCID iDs

Cairns, J.W.P. ORCID logoORCID: https://orcid.org/0000-0002-4036-4760, McPherson, N.A. ORCID logoORCID: https://orcid.org/0000-0003-4008-8724 and Galloway, A.M. ORCID logoORCID: https://orcid.org/0000-0003-0143-8314;
CORE (COnnecting REpositories)