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

Abstract

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.

A comparative study between weldability of polycarbonate and nylon-6 using different pin geometries in friction stir welding

“…Thus, it may be concluded that there was an axial thrust force reduction with more torque requirement at high tool rotational speeds as noticed in previous works. 13,23,24,27 However, the thrust force with respective torque reduction was more predominant for PC and N6, respectively.…”

“…The weld peak temperature is the highest temperature of the weld thermal cycle which comprised of heating and followed by cooling of the weld bead. The weld cooling rate that is, the slope of weld cooling curve just after welding has been determined at 25 second (s) later just after reaching weld peak temperature time 24,26 that is, weld peak temperature to instantaneous temperature at 25 s intervals after reaching peak temperature, which may be expressed as…”

“…The dynamic to static volume ratio of FSW pin is highly subjective to the flow path of soft heated weld material from leading to trailing edge of the revolving tool. 13,22–24…”

Variation Mechanism of Three-Dimensional Force and Force-Based Defect Detection in Friction Stir Welding of Aluminum Alloys

“…Moreover, it has been reported that the frequency spectrum of forces could be used for detection of defects. According to the research reported by Sahu et al [ 15 ], compared with the temperature signals measured by K-type thermocouples, the axial force better predicted the tensile strength. Boldsaikham et al [ 16 ] used discrete Fourier transform and a multi-layer neural network to analyze the force signal, and proposed a nondestructive testing method for tunnel defects in the FSW process.…”

Comparative assessment on weldability of Al 6061 to polycarbonate for dissimilar sheets placement in friction stir lap welding using sensor signals

“…Thus, multi-sensor based process monitoring and weld quality prediction have been proposed in arc welding 29 as well as friction welding techniques. 30 Thus, the tool assisted axial thrust with material stirring torque during friction stir welding are extremely important for altered parametric conditions in thermoplastic polycarbonate 30 and different tool tilt angles in aluminium alloys. 31 The average axial pressure is higher during tool pin plunging over shoulder plunging due to higher frictional heat generation in FSW of polycarbonate.…”