- Weld Quality and Inspection

The objective of this research is to develop a closed-loop control system for robotic friction stir welding (FSW) that simultaneously controls force and temperature in order to maintain weld quality under various process disturbances. FSW is a solid-state joining process enabling welds with excellent metallurgical and mechanical properties, as well as significant energy consumption and cost savings compared to traditional fusion welding processes. During FSW, several process parameter and condition variations (thermal constraints, material properties, geometry, etc.) are present. The FSW process can be sensitive to these variations, which are commonly present in a production environment; hence, there is a significant need to control the process to assure high weld quality. Reliable FSW for a wide range of applications will require closed-loop control of certain process parameters. A linear multi-input-multi-output process model has been developed that captures the dynamic relations between two process inputs (commanded spindle speed and commanded vertical tool position) and two process outputs (interface temperature and axial force). A closed-loop controller was implemented that combines temperature and force control on an industrial robotic FSW system. The performance of the combined control system was demonstrated with successful command tracking and disturbance rejection. Within a certain range, desired axial forces and interface temperatures are achieved by automatically adjusting the spindle speed and the vertical tool position at the same time. The axial force and interface temperature is maintained during both thermal and geometric disturbances and thus weld quality can be maintained for a variety of conditions in which each control strategy applied independently could fail.

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Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽

10.3390/ma14133496 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3496

Author(s):

Haijun Wang ◽

Diqiu He ◽

Mingjian Liao ◽

Peng Liu ◽

Ruilin Lai

Keyword(s):

Aluminum Alloy ◽

Friction Stir Welding ◽

Prediction Model ◽

Real Time ◽

Support Vector ◽

Weld Quality ◽

Prediction Ability ◽

Friction Stir ◽

Temperature Signal ◽

2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

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