Friction stir welding is a patented joining process invented in 1991 at The Welding Institute in Cambridge, UK, and further developed to the stage suitable for production. In this process, a wear resistant rotating tool is used to join sheet and plate with different materials such as aluminum, copper, lead, magnesium, zinc, and titanium. This work studies the thermal characteristics of this process and provides a modeling technique based on Neural Network that can be used for real-time control. A thermal feed-back control method is presented to control the process. Using some thermal modeling for the heat distribution during friction stir welding process, this paper displays the complexity of obtaining an accurate design for the thermal feed back control. A three-dimensional transient heat transfer model is developed here for a sequential joining process (Friction Stir Welding-FSW) applied on aluminum parts. A neural network is created based on a set of experiments to predict the spatial and temporal variations in the temperature over the weld seam for different set of input variables. The model includes the dynamic and friction behavior of the rotating spindle and the thermal behaviors of the weld components involved. The significance of this modeling approach is that it captures the movement of the spindle, simulating a sequential joining process along a continuous weld seam. The modeling results are compared with experimental data obtained by thermocouples and infrared camera, and accurately predict the trend of variations in weld temperature. A fuzzy-logic based controller is proposed to regulate the FSW process parameters to maintain the weld temperature within the margin required to ensure the weld quality. This modeling and control system can have applications in manufacturing aluminum parts in automotive and aerospace industry.
Application of Neural Network Techniques in Friction Stir Welding Process