Journal of Intelligent & Robotic Systems

, Volume 83, Issue 2, pp 219–237 | Cite as

Parameter Self-Optimizing Clustering for Autonomous Extraction of the Weld Seam Based on Orientation Saliency in Robotic MAG Welding

  • Yinshui HeEmail author
  • Huabin Chen
  • Yiming Huang
  • Di Wu
  • Shanben ChenEmail author


This paper presents an effective method which needs free parameters as little as possible to autonomously extract the weld seam profile and edges from the molten background in two kinds of weld images within robotic MAG welding. First, orientation saliency detection produced by Gabor filtering nicely highlights the weld seam profile and edges from the molten background. Then, an unsupervised clustering algorithm combing a cluster validity index via an optimization rule, referred to as parameter self-optimizing clustering, is applied to discern the weld seam profile and edges from interference data after the orientation saliency detection result is given threshold segmentation. The validity index is better than the classical ones in two kinds of data sets through considerable tests. Last, two common applications of weld seam identification demonstrate the effectiveness of the proposed method.


Orientation saliency Robotic MAG welding Clustering Weld seam extraction Cluster validity index 


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Collaborative Innovation Center for Advanced Ship and Deep-sea ExplorationShanghaiChina
  3. 3.School of Resources, Environmental & Chemical EngineeringNanchang UniversityNanchangChina

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