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The joining of dissimilar materials like aluminum and steel is becoming an important requirement in automobile industries due to the need for high strength to weight ratio, corrosion resistance and crash resistance. However, the welding of these two materials is very difficult due to difference in physical and chemical properties, especially the melting temperature. Adhesive bonding is used to join these materials in automobile industries to avoid the formation of brittle intermetallics (FenAlm) (Figner et al., 2009). This problem can be overcome by friction stir spot welding (FSSW) process, a variant of friction stir welding (FSW) process, patented by The Welding Institute (TWI), UK (Thomas et al., 1991). In FSSW, the temperature needed to join aluminum with steel is below the solidus line of aluminum alloy (AA6061), and, so, FSSW provides a very important advantage for dissimilar joining than the conventional fusion welding process (Dupont et al., 2003; Sato et al., 2005; Uzun et al., 2005).
The principle of FSSW process is illustrated in Figure 1. A non-consumable rotating tool with (probe) pin plunges into the upper sheet first and then into the lower sheet (Figure 1a). The tool rotational speed and axial force are maintained for a few minutes (dwell time) to generate frictional heat between tool shoulder and work piece (Figure 1 b). Due to frictional heat, the softened material adjacent to the tool deforms plastically, and a solid state bond is produced between upper and lower sheets. Finally, the tool is withdrawn (Figure 1c) (Wang & Lee, 2007).
Figure 1. Principle of FSSW process
FSSW process parameters such as tool rotational speed, plunge rate, dwell time, and tool diameter ratio influence the mechanical and metallurgical properties of the joints. In order to attain superior mechanical properties, it is necessary to optimize the FSSW process parameters. One of the most efficient methods to optimize the FSSW process parameters is Response Surface Methodology (RSM), RSM is a collection of statistical and mathematical models, very useful tool to analyze and model engineering problems (Box & Wilson, 1951). In this method, the main objective is to optimize the response surface that is influenced by various FSW process parameters. The steps involved in this method are: (1) designing a series of experimental condition based on the factors and their levels, (2) deriving a mathematical model using second order equation with best fit, (3) finding the optimum process parameters that produces a maximum response value, and, (v) indicating the direct and interaction effect of process parameters through two or three dimensional plots (Aslan N (2008); Yia et al., 2010).