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MLD Classification Model of Visual Features of Multi-layer and Multi-pass Molten Pool During Robotic MAG Welding of Medium-Thick Steel Plates

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Transactions on Intelligent Welding Manufacturing (RWIA 2020)

Part of the book series: Transactions on Intelligent Welding Manufacturing ((TRINWM))

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Abstract

Molten pool image segmentation and feature extraction based on vision sensor is one of the core tasks of robotic automated welding. The Geodesic active contour model (GAC) method is used for the molten pool image in the multi-layer and multi-pass welding process (non-swing welding), and the molten pool contour can be effectively separated from the two-dimensional image obtained by the welding vision sensor. Through further analysis of the extracted contour, calculation of melting width, comprehensive evaluation of the radius of curvature of the front, upper and lower ends of the molten pool, the molten pool in multi-layer multi-pass welding can be divided into seven types. Corresponding to the seven forming conditions in multi-layer and multi-pass welding, the MLD classification model is established. The experimental results show that the image segmentation method based on GAC can effectively obtain the edge of MAG weld pool. The characteristics of weld pool can be exactly corresponding to the seven types of multi-layer and multi-pass, which lays a foundation for the MLD dynamic control of welding.

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Acknowledgements

This work is partly supported by the National Natural Science Foundation of China under the Grant No. 61873164.

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Authors and Affiliations

  1. Intelligentized Robotic Welding Technology Laboratory, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Hao Zhou, Huabin Chen & Shanben Chen

  2. School of Resources Environmental and Chemical Engineering, Nanchang University, Nanchang, 330031, China

    Yinshui He

  3. Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Huabin Chen & Shanben Chen

Authors
  1. Hao Zhou
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  2. Yinshui He
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  3. Huabin Chen
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  4. Shanben Chen
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Corresponding authors

Correspondence to Huabin Chen or Shanben Chen .

Editor information

Editors and Affiliations

  1. School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shanben Chen

  2. Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, USA

    Yuming Zhang

  3. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Zhili Feng

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Zhou, H., He, Y., Chen, H., Chen, S. (2022). MLD Classification Model of Visual Features of Multi-layer and Multi-pass Molten Pool During Robotic MAG Welding of Medium-Thick Steel Plates. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. RWIA 2020. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-19-3902-0_4

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