Modeling of Friction Stir Welding of AL7075 Using Neural Networks

Introduction

The friction stir welding was invented by The Welding Institute, UK in 1991 (Thomas et al., 1991). It is a solid state welding and was initially utilized for joining of aluminum alloys (Ozarpa et al., 2005). In FSW, a rotating shouldered tool plunges into the butt plates and locally plasticizes the joint region during its movement along the joint line. In this process, heat is originally derived from the friction between the welding tool (including the shoulder and the probe) and the material to be welded. The heat generated causes the material to soften at a temperature less than its melting point temperature and joining of the plates take place. Friction stir welding can be used for joining of many types of materials and metals, if suitable tool materials and their designs for the work pieces are identified. In FSW process, the tool axis is typically tilted by a few degrees (2^o or 3^o) from the vertical in order to facilitate consolidation of the weld. The process has been used for manufacture of butt welds, overlap welds, T-sections and corner welds. It is to be noted that for each of these geometries, specific tool designs are required (Minton & Mynors, 2006). Moreover, the micro structural changes in friction stir welding was studied by Nelson et al. (2003). The authors had observed a pancake microstructure with unrecrystallized regions consists of rod-like or bar-like shaped sub regions in the parent material. Ericson and Sandstorm (2003) concluded that the fatigue strength of FS welded Al-Mg-Si alloy 6082 was higher than that of MIG-plus and TIG welds of the same material, to the narrower geometry of the joints.

In FSW heat is generated by a combination of friction and plastic dissipation during plastic deformation of the metal. Early experimental studies showed that the majority of the heat generation occurs at the shoulder/work piece interface (Tang et al., 1998). A thermal model (Song & Kovacevic, 2003) for FSW was developed utilizing multiphysics finite element package. For modeling this problem, a fixed tool approach was employed by moving the work piece towards the tool. The tool tilt angle was assumed to be zero. It was observed that the aluminum alloy had temperature dependent yield strength. In Elangovan and Balasubramanian (2007), the authors studied the influence of pin profile and rotational speed of the tool of FSW welding on AA2219 aluminum alloys. Five different tool pin profiles, such as straight cylindrical, tapered cylindrical, threaded cylindrical, triangular and square were used to fabricate the joints. It was observed that square tool pin produces mechanically sound and defect free welds compared to other tool pin profiles.

Later on, statistical regression analysis models based on design of experiments (Montgomery, 2001) were developed to study the influence of various process parameters on the quality of the FSW joint. Full-factorial design of experiments was used by Jayaraman et al. (2009) to analyze the effect of rotational and transverse speeds and axial tool force on the tensile strength of the FSW joint. Lakshiminarayanan et al. (2008) utilized the Taguchi method to optimize the FSW process parameters using a tensile test specimen. Moreover, a complete review of Taguchi optimization of process parameters in friction stir welding was explained in Nourani, Milani, and Yannacopoulos (2011). It is important to note that response surface methodology was also used to model the fusion welding process (Gunaraj & Murugan, 1999), and allows the development of an empirical methodology. Moreover, genetic algorithm (Nandan et al., 2007; Tutum & Hattel, 2010) was used for the optimization of the process parameters after utilizing the regression equations obtained from the statistical modeling methods.