• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2018, Vol. 54 ›› Issue (18): 55-61.doi: 10.3901/JME.2018.18.055

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Analysis of Influence Factors and Multivariable Regression Prediction of Penetration for Automatic GMAW

MA Ke1, XUE Long1, HUANG Junfen1, HUANG Jiqiang1, ZOU Yong1, JIANG Tiansheng2   

  1. 1. College of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617;
    2. The 699 factory, Second Research Institute of China Aerospace Science And Industry Corporation, Beijing 100854
  • Received:2017-12-19 Revised:2018-03-26 Online:2018-09-20 Published:2018-09-20

Abstract: No penetration and "Excessive penetration" are easy to appear in automatic backing GMAW, which becomes the bottleneck of welding automation. Taking the back-bead width as the reference information characterizing the penetration state, the influences of the 4 welding parameters of welding current, welding speed, groove gap and angle on the back-bead width are studied. The backing GMAW tests are carried out, and the data analysis of the results show that, with the increase of the welding current, the back-bead width increases, and the penetration state changes from "No penetration" to "Excessive penetration"; with the increase of the welding speed, the back-bead width decreases, and the penetration state changes from "Excessive penetration" to "No penetration"; the back-bead width increases with the groove gap or angle, and the small gap and angle are easy to cause "No penetration". Since the back-bead width increases or decreases monotonously with the increase of each welding parameter, a mathematical model of the 4 welding parameters and back-bead width is established by the multivariable linear regression, which passed tests of Goodness of Fit, Overall Significance of Regression and Significance of Variables. The mathematical model is verified by GMAW tests, and the results showed that the mathematical expression of the regression model can predict the back-bead width accurately. The study provides guidance for the parameters adjustment of automatic backing GMAW.

Key words: automatic welding, back-bead width prediction, backing welding, gas metal arc welding, multivariable regression model

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