The response of force characteristic to weld-forming process in friction stir welding assisted by machine learning

Abstract

In this study, the response of welding force characteristic to weld-forming process in friction stir welding (FSW) was systematically investigated. Assisted by machine learning technique, we found that both the force value and some detailed characteristics of force waveform are very important in reflecting the characteristics of weld formation. The mapping relations between force characteristics and weld/defect characteristics were comprehensively summarized for the first time. The results show that the distortion of the force waveform signifies the formation of defect, and the distortion degree and deflection direction of the force waveform show high relevancy to the size and location of defect. Meanwhile, the force waveform is decomposed into three standard sinusoidal waves with different frequencies, and the three sinusoidal waves correspond to the formation of the periodic weld microstructures in nugget zone. The causes of force fluctuations, with associated material deformation and flow behaviors during the FSW process, were also clarified to explain the above correspondences. The main reason is that force characteristics are closely related to the tool motion, probe geometric profile and welding parameter, which are key in heat generation and control the material flow in FSW. Therefore, it is concluded that the welding force characteristic is powerful in revealing some key information of the complicated weld-forming process, which can help us to make a deep understanding of FSW and develop intelligent FSW technologies.