Digital twin-based monitoring of physical quantities and joint mechanical properties in friction stir welding

Given the complexities of friction stir welding (FSW), traditional monitoring methods often fail to provide comprehensive real-time insights. By leveraging advanced digital twin technology, this study proposes an innovative system tailored for FSW. This system is capable of real-time monitoring of the temperature and axial force, as well as predicting the tensile strength and microhardness of the joint. By establishing a prediction model for joint mechanical properties based on multi-source data fusion techniques, the system achieves real-time processing and analysis of force and thermal data, enabling online prediction of joint mechanical properties. The integration of the prediction model, MySQL database, 3D visualisation and virtual-real data interaction significantly enhances the capability of the system for dynamic evaluation of physical quantities and joint mechanical properties in real time. The validity of the proposed method and the developed system is verified by experiments. This research provides valuable insights for optimising FSW process control. This work advances digital twin applications in solid-state welding by bridging the gap between multi-physics monitoring and real-time quality prediction.