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Wang Rong

at Jiangsu Univ

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Proceedings Article | 13 November 2019 Paper

Prediction of weld penetration status based on sparse representation in fiber laser welding

Zhenying Xu, Rong Wang, Rong Zuo

Proceedings Volume 11343, 113431U (2019) https://doi.org/10.1117/12.2548813

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Butt welding is the typical welding mode for the fiber laser welding, and penetration status of the weld is critical point to assess welding quality. For the sake of solving the prediction of the penetration status in the fiber laser welding, a sparse representation prediction model was established to monitor the welding process. The sparse representation classification algorithm of using the K-SVD algorithm constructed the dictionary was used to predict the weld penetration status. However, the dictionary trained by K-SVD algorithm was not discriminative and the prediction accuracy was low. A D-KSVD algorithm with the discriminant dictionary learning mode was proposed, and the initialization method of the initial dictionary was improved to enhance the dictionary discriminant performance. The experiment result indicates the average recognition accuracy of the improved D-KSVD algorithm is 4 percentage points higher than the D-KSVD algorithm, and the accuracy of the weld penetration status prediction can reach 0.943, which shows that the recognition accuracy of the DKSVD algorithm is significantly higher than the K-SVD algorithm, and the dictionary learning with adding the discriminant learning can effectively improve the prediction of weld penetration status.

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