Seam Tracking Monitoring Based on Adaptive Kalman Filter Embedded Elman Neural Network During High-Power Fiber Laser Welding | IEEE Journals & Magazine | IEEE Xplore

Seam Tracking Monitoring Based on Adaptive Kalman Filter Embedded Elman Neural Network During High-Power Fiber Laser Welding


Abstract:

This paper proposes a method of seam tracking monitoring during high-power fiber laser welding. A visual sensor system was employed to capture the infrared images of molt...Show More

Abstract:

This paper proposes a method of seam tracking monitoring during high-power fiber laser welding. A visual sensor system was employed to capture the infrared images of molten pools and the surroundings in the laser welding process. A weld seam position variable was extracted by the image difference and centroid algorithms. The state and measurement equations for weld seam position were established based on an eigenvector derived from the weld seam position variable. A Sage-Husa adaptive Kalman filter (AKF), as an estimator of the noise statistical characteristics, was applied in order to enhance the filtering precision. By embedding an Elman neural network into the AKF, an error estimator was used to compensate for the filtering errors. The results of the welding experiments have demonstrated the effectiveness of the proposed method to improve the accuracy of weld detection.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 59, Issue: 11, November 2012)
Page(s): 4315 - 4325
Date of Publication: 09 April 2012

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