The invention discloses a
defect repair method based on digital twinning and
friction stir welding technologies, and relates to the technical field of intelligent manufacturing and digital twinning, and the
defect repair method comprises the following steps: synchronously capturing full-dimensional data of a
welding area through a multi-mode
sensor array integrated by an
actuator; secondly, segmenting defect boundaries by adopting a
deep learning algorithm, constructing a dynamic twin model in combination with thermal-force field
coupling simulation, and accurately mapping defect three-dimensional features; generating a repair track according to the twinborn model, converting the repair track into a
robot joint instruction through a curved surface parameterization
mapping algorithm, and implanting real-time anti-collision constraint; in the repairing process, based on
reinforcement learning control of the material rheological resistance and the
temperature gradient, the rotating speed, the advancing speed and the down force of the tool are dynamically adjusted; and after repairing, micro-focus CT scanning is started immediately, actually measured data is compared with twinborn prediction, and when the deviation exceeds a threshold value, a re-repairing process is triggered automatically. The method solves the problems that a traditional method depends on manual intervention and the precision of a sensor is easily interfered by the environment.