## 1 Introduction  
Austenitic stainless steels are used in the construction of parts for automobiles and boats due to their high corrosion resistance and mechanical performance [1]. They are also used in nuclear, aeronautical, food, petrochemical, and biological applications [2, 3, 4] due to their robust mechanical, physical, and chemical properties [5, 6]. To achieve these parts or elements, different manufacturing processes [7] that allow the welding to stand out [8] must be used. Welding processes have an important role in the manufacturing of engineering parts and components due to their simplicity, versatility, and automation capability. However, the welding joints are often the weakest links in assemblies [9]. The quality of the weld depends on the bead geometry [10], metallurgical and mechanical characteristics [11], chemical composition [12], and input parameters such as voltage, current, electrode type, flux and gas type, welding position, travel speed, and others [4] making the task of finding the best parameters quite challenging [13]. If the welding parameters are not correctly chosen, they can lead to welded joints with high levels of residual stresses [14, 15], geometric distortions [16], and discontinuities [17, 18]. In stainless steels, excess heat in the welding process can lead to microstructural changes with sensitization, the evolution of secondary phases, and hot cracks [2, 19]. Suchproblems are especially problematic because of the high-responsibility applications to which the materials are often applied [6]. Many studies have focused on the best way to carry out the connection of thin austenitic stainless-steel sheets. Different tools and input parameters were tested for the best results when welding this material,. [PERSON] et al. [1], [PERSON] and [PERSON] [5], and [PERSON] et al. [19] analyzed parameter changes in laser welds through micrography and mechanical properties. Other authors such as [PERSON] and [PERSON] [20] and [PERSON] et al. [2] proposed the union of thin austenitic steel plates by Friction Stir Welding (FSW), analyzing the mechanical and metallographic properties of the welded joints with the variation of input parameters. Despite attempts to try alternative welding processes, GMAW welding has been used in many industries due to its ease of fabrication in a wide range of materials with a low initial investment [21]. In this sense, [PERSON] et al. [22] compared MIG (Metal Inert Gas) welding and laser-MIG hybrid welding on Nitrogen-rich austenitic steel, analyzing mechanical and microstructural properties of the weld bead. [PERSON] et al. [23] performed the welding of stainless steel plates varying the type of flux material and the geometry of the MIG weld bead. [PERSON] et al. [21] studied the variation of parameters in pulse-MIG welding, considering penetration and dilution in the process, and developing a regression model between inputs and outputs using statistical tools. There are several methods for optimizing input parameters, the main ones being: factorial design; linear regression; response surface methodology; artificial neural networks; finite elements methods; and the Taguchi method [24; 25]. The Taguchi method is a simple and low-cost method of obtaining process optimization with strong quality and performance. The technique greatly reduces the number of experiments needed and offers a good relationship between input and output parameters. Some studies used the Taguchi method to identify the most relevant parameter: [PERSON] et al. [26] analyzed the grain refinement in weld beads of a ferritic stainless steel welded by the Gas Tungsten Arc Welding (GTAW) process. The Taguchi method was used to create regression equations and predict mechanical properties. [PERSON] et al. [27] varied three parameters of Tungsten Inert Gas (TIG) welding and evaluated which one had the greatest contribution to tensile strength using the Taguchi method. [PERSON] et al.