Evaluate effect of pulsed current gas tungsten arc welding process parameter on intergranular corrosion of ss304l weld

Austenitic stainless steel (ASS) is the most common type of stainless steel which offers excellent weldability and mechanical properties. ASS is being used for various applications i.e. automotive, oil and gas and chemical industries in which the welding process plays a prominent role. Welding process selection is the main factor that emphasizes mechanical and corrosion resistance properties in various aggressive environments. There are various corrosion occurs in ASS but intergranular corrosion (IGC) forms during welding at elevated temperatures. IGC mainly occurs at grain boundaries of structure and resulting chromium depletion due to precipitation of chromium carbide at the grain boundary. In present work pulsed current gas tungsten arc welding (PCGTAW) process was used to investigate intergranular corrosion by oxalic acid test as per ASTM A262 Practice A. Experiments performed based on Taguchi L9 using design of experiments and corrosion rates are evaluated at base metal, heat affected zone and weld zone. This work is aimed to optimize process parameters followed by regression analysis to IGC susceptibility in the weldment. In this investigation, it has been found from ANOVA and main effects plots that peak current and base current are the most significant parameters in the PCGTAW process. The results of the corrosion test revealed that heat affected zone is more susceptible to IGC. At the end, it has been observed that the optimum value of peak current, base current and frequency based on regression analysis are 100 A, 50 A and 6 Hz respectively

Automatic Control of the Weld Bead Geometry

Automatic control of the welding process is complex due to its nonlinear and stochastic behavior and the difficulty for measuring the principal magnitudes and closing the control loop. Fusion welds involve melting and subsequent solidification of one or more materials. The geometry of the weld bead is a good indicator of the melting and solidification process, so its control is essential to obtain quality junctions. Different sensing, modeling, estimation, and control techniques are used to overcome this challenge, but most of the studies are using static single-input/single-output models of the process and focusing on the flat welding position. However, theory and practice demonstrate that dynamic models are the best representation to obtain satisfactory control performance, and multivariable techniques reduce the effect of interactions between control loops in the process. Also, many industrial applications need to control orbital welding. In this chapter, the above topics are discussed

REAL-TIME SENSING AND CONTROL OF DEVELOPING WELD PENETRATION THROUGH REFLECTION VIBRATION IN GTAW

GTAW (Gas Tungsten Arc Welding) weld pool surface is believed to contain sufficient information to determine the weld penetration, from which skilled welders are able to control the welding process to desired penetration states. However, it is unclear how human welders extract the weld penetration from the observed weld pool surface. In this research, a novel method is studied to determine the weld penetration based on the dynamic change of the weld pool surface. This study observes/measures/analyzes the development of a weld pool from partial to full penetration and correlates such change to the weld penetration. Similar trends in the weld pool surface are observed when the weld penetration changes from partial to full penetration despite the amperage used and material welded. Correlating the weld pool surface reflecting grayness and the development of the weld penetration from experiments shows: (1) the weld pool reflection intensity will increase while the weld penetration is increasing; (2) the increasing trends of weld pool reflection intensity will decrease when the full penetration is achieved; (3) the weld pool reflection intensity will increase after the full penetration is achieved. Such trend in the weld pool surface reflection intensity when the weld penetration develops is used as feedback signal to detect the weld pool penetration. To control the weld pool penetration, a first-order dynamic model is identified. Model Predictive Control (MPC) is used to control the weld penetration. Experiments verified the feasibility of this proposed method and established system