Gas metal arc welding

About: Gas metal arc welding is a research topic. Over the lifetime, 11706 publications have been published within this topic receiving 109555 citations. The topic is also known as: metal active gas welding & GMAW.

Metallurgical characterization of the 5083H116 aluminum alloy welded with the cold metal transfer process and two different wire-electrodes (5183 and 5087)

Abstract: Aluminum-magnesium alloys have great prominence in the naval sector as they represented structural materials with low specific weight and excellent corrosion resistance in marine environments. Such benefits are also seen in other maritime structures, as oil platforms. Welding is the main manufacturing process in this sector. However, there are substantial technological challenges when welding these alloys. The work undertaken in this paper aimed at the development of welding science and technology in the specific framework of the construction project of an oceanographic sailboat of naval aluminum alloy 5083. This paper presents a technical and scientific contribution for the short-circuiting gas metal arc welding (GMAW) process version designated as cold metal transfer (CMT). As to the CMT mode, work intended to verify the compatibility of existing synergic programs in the welding equipment with the investigated wire-electrodes 5183 and 5087 and perform needed adaptations, as there are no programs designed specifically for these materials. The focus was on obtaining root and fill pass on 6.0-mm-thick plates. The work also examined the influence of this process on metallurgical effects for the distinct wires and compared the properties between them. Welding procedure development and mechanical performance tests, destructive and non-destructive, were conducted. The results show excellent performance of welded joints with both wires, and a slightly higher mechanical performance with 5087 wire-electrode.

Application of integrated soft computing techniques for optimisation of hybrid CO2 laser-MIG welding process

Abstract: Three integrated ANN-GA, ANN-SA and ANN-Quasi Newton methodology has been developed and implemented according to the following way to determine optimised input parameter setting for maximum welding strength during laser-MIG hybrid welding of aluminium alloy plates. Finally, significance of optimised parameters has been determined by ANOVA. Variation of welding strength with individual process parameters have been tested through main effect plots and interaction plots. Three soft computing based integrated models such as, ANN-GA, ANN-SA and ANN-Quasi Newton have been developed.Those models predicted and optimised welding strength during hybrid CO2 laser-MIG welding process.Best ANN architecture (3-11-1 network) predicts welding strength with mean absolute percentage errors less than 2%.ANN-GA shows best optimisation performance with only 0.09% experimental validation error.Welding speed shows maximum influence on welding strength and an increase in welding speed decreases welding strength. In this paper, artificial neural networks (ANNs), genetic algorithm (GA), simulated annealing (SA) and Quasi Newton line search techniques have been combined to develop three integrated soft computing based models such as ANN-GA, ANN-SA and ANN-Quasi Newton for prediction modelling and optimisation of welding strength for hybrid CO2 laser-MIG welded joints of aluminium alloy. Experimental dataset employed for the purpose has been generated through full factorial experimental design. Laser power, welding speeds and wires feed rate are considered as controllable input parameters. These soft computing models employ a trained ANN for calculation of objective function value and thereby eliminate the need of closed form objective function. Among 11 tested networks, the ANN with best prediction performance produces maximum percentage error of only 3.21%. During optimisation ANN-GA is found to show best performance with absolute percentage error of only 0.09% during experimental validation. Low value of percentage error indicates efficacy of models. Welding speed has been found as most influencing factor for welding strength.