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Estimation of Arc Welding Pressure Pipeline Weld Peaking Parameters Based on Data Prediction

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  • Pu Liao
  • Guixiong Liu
  • Ningxiang Yang

Abstract

Peaking parameter is the key content in the regular inspection of the pressure pipeline. Solving the problem of the peaking measurement method defined by a standard cannot be applied to a situation in which there exists a weld surface with reinforcement and misalignment. In this paper, a peaking estimation method based on data prediction was proposed to estimate the contour information of the base metal at the weld joint using the contour point set data of the base metal part of the weld. Herein, the longitudinal weld peaking estimation method based on a piecewise logistic regression (PLR) and the girth weld peaking estimation method based on a piecewise Bayesian linear regression (PBLR) were studied, and the midpoint of the two symmetrical points of the base metal on either side of the weld was used as a reference for calculating the peaking. Finally, we collected the surface profile data of longitudinal weld pressure pipes with diameters of 155 mm, 255 mm, 550 mm, and 600 mm and the surface profile data of girth weld pressure pipes with diameters of 120 mm, 130 mm, 140 mm, and 170 mm. This weld seam data used the data estimation method proposed in this article and traditional long short-term memory and fitting methods. The results showed that the proposed data prediction method could accurately predict the position of the base metal, and the theoretical mean absolute error of the peaking estimation based on the PBLR and PLR could attain 0.06 mm and 0.07 mm, respectively, which meets the parameter measurement requirements of related verification fields.

Suggested Citation

  • Pu Liao & Guixiong Liu & Ningxiang Yang, 2021. "Estimation of Arc Welding Pressure Pipeline Weld Peaking Parameters Based on Data Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, December.
  • Handle: RePEc:hin:jnlmpe:5073562
    DOI: 10.1155/2021/5073562
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