Multi sensor based strategies for accurate prediction of friction stir welding of polycarbonate sheets

High performance thermoplastic is often used in automotive and aerospace sectors. Friction stir welding (FSW) has been a suitable method for joining these thermoplastic materials. However, the process characteristics vary dynamically, even at same parametric conditions due to process disturbances. While quite a few studies have been reported in experimental investigation, the usage of sensors for the process monitoring during welding has not been explored. Therefore, the present paper focuses on various sensor-based approaches on process monitoring. The study includes an in-depth experimental investigation for joining of polycarbonate sheets using full factorial design of experiments. Three different tool pin profiles like cylindrical, square and triangular have been selected, whereas tool rotational speed and traverse speed are considered as primary process variables. Various statistical time domain features of the force and torque signals starting from average, its deviation and root mean square as well as thermal cycle based weld peak temperature and its cooling rate were found to have a strong correlation with weld bead profile. The axial force and thermal cycles were found to be the major indicators of joint strength efficiency rather than torque as per regression models developed by using response surface methodology. Finally, an attempt has been made for improving the joint strength predictability by using various sensor-based approaches. This work can be used potentially to real time welding process monitoring in industrial sectors.