This work compares the welding residual stresses of the cold-wire gas metal arc welding and conventional gas metal arc welding processes. Two techniques were used to measure the residual stresses: X-ray diffraction and acoustic birefringence. The base metal used was carbon manganese steel plates of 9.5-mm thickness. The results showed that the introduction of the cold-wire tends to decrease the residual stresses, suggesting that the introduction of the cold wire decreases the amount of heat given to the base metal, and consequently lowers residual stresses.

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The fatigue strength of weld joints manufactured using gas metal arc welding and cold wire GMAW (CW-GMAW) was evaluated under stress-controlled cyclic loading. The material used in this study was class ASTM 131 grade A steel, joined using ER70S wire filler metal. The addition of cold wire led to a decrease in the amount of intergranular ferrite and an increase in hardness in the heataffected zone. The assessment of fatigue life was performed by using the Weibull distribution and the results revealed that with a 99% reliability the joints produced using the CW-GMAW process have a longer fatigue life, especially as the stress amplitude increases.

The influence of current profile and pulse parameters on droplet formation and transfer was investigated. One profile has an exponential ramp up and down in the current pulse shape, while the second is nearly square shaped. High-speed photography, synchronised with a high-speed data acquisition system, was used to monitor the droplet formation and transfer. It was found that for long-tail current profile, most of droplet formation and detachment occurs before background current is reached. While, for the nearly square pulse, most of droplet formation and transfer occurs during background current, giving a stable and smooth metal transfer. The arc attachment position was found to vary for the different profiles. Droplet speed was measured, and it was found that it is proportional to the peak current and inversely proportional to background current. Dimensionless process parameters were defined and used to predict droplet speed using a neural networks algorithm.