Bobbin Tool Friction Stir Welding of Aluminum: Parameters Optimization Using Taguchi Experimental Design

Materials (Basel). 2022 Apr 9;15(8):2771. doi: 10.3390/ma15082771.

Abstract

This work aims to optimize the performance evaluation characteristics such as the temperature at the weld center of the lap joint (Tw), the tensile shear load (TSL), and the hardness using an experimental design experiment for bobbin tool friction stir welding (BT-FSW) of AA1050 lap joints. BT-FSW is characterized by a fully penetrated pin and double-sided shoulder that promote symmetrical solid-state welds. This study contributes to improving the quality of 10 mm thick lap joints and addressing challenges to obtaining a sound weld deprived of any defects. Taguchi L9 orthogonal array (OA) experimental design was performed. Three different pin shapes (cylindrical, triangular, and square) and three levels of welding travel speeds of 200, 400, and 600 mm/min were selected as input controllable process parameters at a constant tool rotation speed of 600 rpm. A travel speed of 200 mm/min with square pin geometry significantly improves the TSL of the joint up to 6491 N. However, the hardness characteristic is optimized by using 600 mm/min travel speed and a cylindrical tool pin. The minimum temperature in the weld joint can be obtained using 600 mm/min or more with triangular pin geometry. From ANOVA results, it was seen that the BT-FSW of AA 1050 thick lap joints performance in terms of TLS and Tw were greatly influenced by travel speed; however, the tool shape influences the hardness more. For the validation of the models, BT-FSW experiments have been carried out for AA1050 using the applied processing parameters. Furthermore, regression models were developed to predict the Tw, TSL, and hardness. The calculated performance properties from the mathematical models were in an acceptable range compared to the measured experimental values.

Keywords: AA1050; ANOVA; Taguchi design; bobbin tool; friction stir welding; mechanical properties; regression model.