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Article

Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding

College of Electrical and Electronic Engineering, Shandong University of Technology, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2445; https://doi.org/10.3390/app8122445
Received: 3 November 2018 / Revised: 26 November 2018 / Accepted: 29 November 2018 / Published: 1 December 2018
(This article belongs to the Special Issue Intelligent Imaging and Analysis)
There is a strong correlation between the geometry of the weld pool surface and the degree of penetration in arc welding. To measure the geometry of the weld pool surface robustly, many structured light laser line based monitoring systems have been proposed in recent years. The geometry of the specular weld pool could be computed from the reflected laser lines based on different principles. The prerequisite of accurate computation of the weld pool surface is to segment the reflected laser lines robustly and efficiently. To find the most effective segmentation solutions for the images captured with different welding parameters, different image processing algorithms are combined to form eight approaches and these approaches are compared both qualitatively and quantitatively in this paper. In particular, the gradient detection filter, the difference method and the GLCM (grey level co-occurrence matrix) are used to remove the uneven background. The spline fitting enhancement method is used to remove the fuzziness. The slope difference distribution-based threshold selection method is used to segment the laser lines from the background. Both qualitative and quantitative experiments are conducted to evaluate the accuracy and the efficiency of the proposed approaches extensively. View Full-Text
Keywords: Image processing; segmentation; spline; grey level co-occurrence matrix; gradient detection; threshold selection Image processing; segmentation; spline; grey level co-occurrence matrix; gradient detection; threshold selection
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MDPI and ACS Style

Wang, Z.; Zhang, C.; Pan, Z.; Wang, Z.; Liu, L.; Qi, X.; Mao, S.; Pan, J. Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding. Appl. Sci. 2018, 8, 2445. https://doi.org/10.3390/app8122445

AMA Style

Wang Z, Zhang C, Pan Z, Wang Z, Liu L, Qi X, Mao S, Pan J. Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding. Applied Sciences. 2018; 8(12):2445. https://doi.org/10.3390/app8122445

Chicago/Turabian Style

Wang, Zhenzhou, Cunshan Zhang, Zhen Pan, Zihao Wang, Lina Liu, Xiaomei Qi, Shuai Mao, and Jinfeng Pan. 2018. "Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding" Applied Sciences 8, no. 12: 2445. https://doi.org/10.3390/app8122445

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