研究目的
To develop a quality inspection system for robotic laser welding that can handle double-curved geometries, enabling the detection of changes in the welding trajectory relative to the joint position with high accuracy.
研究成果
The proposed monitoring system successfully estimates the welding trajectory and joint position with high accuracy, detecting changes in the trajectory relative to the joint position within ± 0.2 mm. The system shows promise for quality inspection in robotic laser welding of double-curved geometries but requires further validation under diverse conditions.
研究不足
The system's accuracy is within ± 0.2 mm, and it does not account for changes in the axial direction of the laser focal point. Further testing under varied scenarios is needed to fully evaluate robustness.
1:Experimental Design and Method Selection:
The study employs a combination of a CMOS camera mounted to look through laser optics, external LED illumination, and optical filters for data acquisition. Template matching and a Kalman filter are used for estimating the 2D displacement field between images to determine the welding trajectory. A Canny edge detector and Hough transform are applied to determine the joint location.
2:Sample Selection and Data Sources:
The experiment involves autogenous robotic laser welding of pre-worked thin stainless-steel metal plates with double-curved T-joint configurations.
3:List of Experimental Equipment and Materials:
Includes a TruDisk 4001 solid-state laser, a Permanova WT04 ST optics with a motorized twin spot unit, a KUKA KR 30 HA 6D robot, and a Basler acA645-100gm CMOS camera.
4:Experimental Procedures and Operational Workflow:
The welding process is monitored in real-time, capturing images around the moving laser focal point. The trajectory and joint position are estimated post-process using the described methods.
5:Data Analysis Methods:
The displacement field between images is estimated using template matching, with data filtered through a Kalman filter. Joint location is determined using edge detection and Hough transform.
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