Welding Journal - June 2012

Shielding gases are fundamental to the operation of the gas metal arc welding (GMAW) process and there are a number commonly used, each with its own specific properties, i.e., ionization potential, which creates unique arc characteristics (Refs. 1–7). Shielding gases are also commonly used in a variety of premixed combinations of two or more gases in order to take advantage of the beneficial properties of each gas (Refs. 8, 9). Recently, however, there has been some positive research (Refs. 1–4) into the effects of alternating shielding gases in both GMAW and, to a lesser extent, gas tungsten arc welding. This method involves discretely supplying two different shielding gases, each with a duty cycle of 50%; i.e., while one gas is flowing the other is not, which results in a continuous shield with varying properties. These studies have shown beneficial results including an increased travel speed, reduced porosity, and increased strength. For example, Campbell et al. (Ref. 1) reported the use of alternating shielding gases can reduce the overall weld cost by approximately 17% while also considerably reducing distortion. Chang (Ref. 2) reported the use of alternating shielding gases created beneficial effects on the weld pool and, as shown in Fig. 1 (Ref. 3), different flow vectors were created in the weld pool for different gases used. However, when alternating between shielding gases, complex flow patterns were created that caused a dynamic action in the weld pool and this is known to be a result of the fluctuation between these individual shielding gas flow vectors. The dynamic nature of the shielding gas delivery is known to be influenced by factors such as (a) arc pressure variation, (b) variation in weld pool fluidity, and (c) arc pressure peaking. As several industry sectors (e.g., shipbuilding and road transportation) move toward thinner and stronger materials in order to reduce the overall mass of the structure (Refs. 10, 11), it is widely recognized that these thinner materials are more susceptible to distortion induced by the heat input generated by the welding processes used during fabrication. Distortion is a result of the nonuniform expansion and contraction of the weld material due to the heating and cooling cycle (Ref. 11) and although computational models can be used to help predict the magnitude of weld-induced distortion (Ref. 12), as a result of the number of variables involved (Ref. 13) including material properties, welding procedure, structural design, and manufacturing procedure (each of which have numerous sub variables) results in such models being specific to the data used in the model generation. However, the effort required to rectify the distortion from the steel structure is highly resource intensive. For that reason it is beneficial to eliminate as much distortion at the source as possible and this is largely achievable through good practices, mainly related to reducing the heat going into the steel structure and the concentration of heat in specific areas. Further, there is an increasing demand for quality prediction in today’s ever-increasing automated soci-