Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding
- 72 Downloads
The robustness of the image processing algorithm is very important based on vision sensor in robotic seam tracking, which will directly affect the accuracy of weld seam shaping quality. Especially in GMAW (Gas Metal Arc Welding), there is a lot of strong noise image. This paper studies an algorithm for the several weld seam images with strong noise in robotic GMAW, such as the atypical weld seam, the strong arc light and the large spatter. Based on a purpose-built visual sensing system, the fast image segmentation, the feature area recognition of the convolutional neural network (CNN), and the feature search technique are used to identify the weld seam features accurately in the algorithm. The selection range of the threshold is increased from 0.5 × 107 to 0.9 × 107 by using the proposed algorithm, which reduces the difficulty of parameter adjustment and increases the stability of seam tracking system. And, the accuracy of the CNN model was 98.0% for the atypical weld seam identification. To evaluate the robustness of the proposed algorithm, the accuracy is verified using experiments on two typical strong noise images. The experiments show that the average error of feature extraction accuracy is 0.26 mm and 0.29 mm. The results show that the proposed algorithm can extract the feature of weld seam image with strong noise accurately and effectively.
KeywordsFeature extraction Strong noise image Robotic GMAW Seam tracking Convolutional neural network Image segmentation
Unable to display preview. Download preview PDF.
This work is partly supported by the Shanghai Natural Science Foundation (18ZR1421500), the National Natural Science Foundation of China under the Grant Nos. 51405298 and 51575349, and the project of Qingpu District, and the State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System (GZ2016KF002).
- 1.Xu Y, Lv N, Fang G, S D, Zhao W, Ye Z, Chen S (2017) Welding seam tracking in robotic gas metal arc welding. J Mater Process Tech 248:18–30Google Scholar
- 4.Ding D (2017) Design of integrated neural network model for weld seam tracking and penetration monitoring. Clust Comput 20(4):3345–3355Google Scholar
- 5.Muhammad J, Altun H, Abo-Serie E (2017) Welding seam profiling techniques based on active vision sensing for intelligent robotic welding. Int J Adv Manuf Technol 88(1–4):127–145Google Scholar
- 7.Gao X, Mo L, Xiao Z, Chen X, Katayama S (2015) Seam tracking based on Kalman filtering of micro-gap weld using magneto-optical image. Int J Adv Manuf Technol 83(1–4):21–32Google Scholar
- 8.Yue L, Guo X, Yu J (2016) An improved method of contour extraction of complex stripe in 3D laser scanning. DEStech Trans Eng Technol Res (ICMITE2016)Google Scholar
- 16.Abdel-Hamid O, Mohamed A, Jiang H, Deng L, Penn YGD (2014) Convolutional neural networks for speech recognition. IEEE-ACM T Audio Spe 22(10):1533–1545Google Scholar
- 17.Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882Google Scholar