Weld Seam Profile Identification with T-Joints Based on Intensity Mutation and Density Feature Detection for Thick Plate Welding Process

doi:10.16183/j.cnki.jsjtu.2020.068

Abstract

Abstract:

There is a need for the effective weld seam profile extraction method to realize the automatic and intelligent welding process with thick steel plates based on laser vision sensing. In this paper, a method was proposed to identify the variable weld seam profiles from the strong arc background based on intensity mutation and density feature detection for the thick plate welding process with T-joints. First, an improved Canny algorithm was used to magnify the weld seam profile and restrain interference. Next, an intensity mutation detection method was proposed to further strengthen the weld seam profile because there exists intensity mutation in the local region surrounding the weld seam profile. Finally, an algorithm based on the band-width and density feature scanning method was proposed to further eliminate the interference data after the strengthened image was binarized with the Otsu algorithm. The weld seam profile was identified as clusters with their spatial scale features after the nearest neighbor clustering was dealt with. The results show that this method can identify over 95% of the weld seam profiles from the arc interference background whose area is about 20% of the image. It provides valuable reference for promoting the automatic and intelligent welding process with different joints.

Key words: intensity mutation, density feature detection, improved Canny algorithm, weld seam profile identification, visual sensing, T-joints

CLC Number:

TG409

HE Yinshui, LI Daize, ZHAO Ziyu, QIAN Weixu. Weld Seam Profile Identification with T-Joints Based on Intensity Mutation and Density Feature Detection for Thick Plate Welding Process[J]. Journal of Shanghai Jiao Tong University, 2021, 55(6): 757-763.

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