Temperature monitoring system of friction stir welding based on digital twin

ABSTRACT

Temperature field distribution of friction stir welding (FSW) influences weld quality directly, so real-time monitoring of the welding temperature is significant. However, it is difficult to monitor the temperature in the core zone due to mechanical obstructions, material plastic deformation and complex thermal-mechanical coupling. To tackle this issue, this study develops a digital twin-based temperature monitoring system of FSW. Initially, a five-dimensional integrated framework based on the digital twin concept is proposed, outlining the process of building a digital twin-based temperature monitoring system of FSW. Subsequently, a motion simulation model of FSW is established, and synchronous motion simulation of the welding process is achieved. Real-time temperature readings from the FSW workpiece surface are gathered via an infrared thermal imager and synchronously transmitted to the FSW temperature monitoring system using socket communication. Additionally, a predictive model utilizing Support Vector Regression (SVR) is incorporated, enabling real-time and precise prediction of extremum temperatures in the core zone and over-limit alarm functionality. Finally, the temperature monitoring system of FSW, grounded in the digital twin concept and integrating the outlined models and features, is developed and experimentally validated. The system enables operators to exercise timely control over the welding process to ensure weld quality.

KEYWORDS:

• Friction stir welding
• monitoring system
• temperature
• digital twin

Acknowledgments

The research was supported by the National Key Research and Development Program of China (Grant No. 2019YFA0709003) and Natural Science Foundation of Liaoning Province of China (2023-MS-101). The financial contributions are gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Key Research and Development Program of China [2019YFA0709003]; Natural Science Foundation of Liaoning Province of China [2023-MS-101].