Modeling and Optimization of Adjustment of Human Welder on Weld Pool Dynamics for Intelligent Robot Welding

Gang Zhang, Yukang Liu, Yu Shi, Ding Fan, Yuming Zhang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

An improved machine–human cooperative control system was developed to obtain sufficient data pairs for modeling welder’s adjustment on weld pool dynamics by data-driven approaches. Spectral analysis shows that weld widths are apparently changed due to low-frequency variations of the welder’s hand movement, which can be filtered by a low-pass filter to remove the high-frequency components. The effect of welding torch orientation on weld pool dynamics was numerically studied to understand the mechanism of human welder’s adjustment and to provide the useful data for control system optimization. A gay multiple linear regression model (GMLRM) was employed to analyze the contribution and interactive compensation of each adjusted parameter on the weld widths as the welding current randomly changes in a given range. A nonlinear adaptive kernel radial basis function neural network (AK-RBFNN) was also proposed to improve the model accuracy. Results indicate that the redundant, coupled, and integrated hand adjustments are adopted to maintain the desired weld pool status, and the human welder’s adjustment reflect nonlinear, complex characteristics. Results also show that the proposed AK-RBFNN model can appraise the weld widths with a good accuracy.

Original languageEnglish
Title of host publicationTransactions on Intelligent Welding Manufacturing
Pages3-26
Number of pages24
DOIs
StatePublished - 2019

Publication series

NameTransactions on Intelligent Welding Manufacturing
ISSN (Print)2520-8519
ISSN (Electronic)2520-8527

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Singapore Pte Ltd.

Keywords

  • GTAW
  • Multiple linear regression
  • Numerical simulation
  • Weld pool dynamics

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering

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