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The effect of nitrogen addition, heat input, and filler metals on weld metal microstructure and mechanical properties of alloy 316 ASS are studied. Autogenous gas tungsten arc welding (GTAW) is employed by adding up to 2vol. % N2 in Ar. These variables affect a number of welding aspects, including arc characteristics and microstructure. The influence of shielding gas mixtures on microstructure and mechanical properties of GTAW of austenitic 316 stainless steel is studied. Mechanical properties of welds are determined through uniaxial tension, hardness measurements, impact, and bending tests. Weld defects, as porosity and inclusions are examined using radiographic testing. Weld specimens are free of porosity, inclusions, and hydrogen cracking. Mechanical properties and cooling rate are lower at higher heat input, but the cooling time, nugget area, and solidification time are higher. The addition of N2 to Ar shielding gas leads to higher values of the ultimate tensile strength ,ÄòUTS,Äô, yield stress ,ÄòYS,Äô, and elongation percent. UTS, YS, and elongation of welds depend on heat input, filler metal, and N2 content of shielding gas. Finally, a mathematical model is built depending upon the welding current, filler metals, and shielding gases.
This monograph presents the outcome of our investigation that was conducted to correlate process variables in shielded metal-arc welding and post weld heat treatment on some mechanical properties of low carbon steel weld. From the results obtained we observed that welding current and post weld heat treatment temperatures influences the tensile strength, impact strength and hardness of welded low carbon steel. As the welding current increases the hardness and strength increases but impact strength reduces, while hardness and strength continuously reduces but impact strength increases as post weld heat treatment temperatures increases. This outcome will help to eliminate much of the "guess work" often employed by welders to specify welding parameters and help them to properly select them for a given task to provide a good weld quality. With this finding welder can now adequately select welding currents and post weld heat treatment temperatures for low carbon steel that will yield quality weld in shielded metal-arc welding without guess work.
In recent years, there have been several attempts to study the effect of critical variables on welding by computational modeling. It is widely recognized that temperature distributions and weld pool shapes are keys to quality weldments. It would be very useful to obtain relevant information about the thermal cycle experienced by the weld metal, the size and shape of the weld pool, and the local solidification rates, temperature distributions in the heat-affected zone (HAZ), and associated phase transformations. The solution of moving boundary problems, such as weld pool fluid flow and heat transfer, that involve melting and/or solidification is inherently difficult because the location of the solid-liquid interface is not known a priori and must be obtained as a part of the solution. Because of non-linearity of the governing equations, exact analytical solutions can be obtained only for a limited number of idealized cases. Therefore, considerable interest has been directed toward the use of numerical methods to obtain time-dependant solutions for theoretical models that describe the welding process. Numerical methods can be employed to predict the transient development of the weld pool as an integral part of the overall heat transfer conditions. The structure of the model allows each phenomenon to be addressed individually, thereby gaining more insight into their competing interactions. 19 refs., 6 figs., 1 tab.
The present research has been done to study the effect of different independent input process parameters on the desired responses in the submerged arc welding process. Half factorial technique has been used for the design of experiments. The effects of welding current, open circuit, welding speed and nozzle to plate distance have been found on the reinforcement, bead width, depth of penetration and width of penetration on 12mm mild steel plates. The effect of all the input parameters on the output responses have been analyzed using the analysis of variance (ANOVA) and mathematical modeling. The developed models could be used for the prediction of important weld bread geometry and control of the weld bead quality by selecting appropriate process parameters.