Dynamic neuro-fuzzy-based human intelligence modeling and control in GTAW

Yukang Liu, Weijie Zhang, Yuming Zhang

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

Abstract

Human welder's experiences and skills are critical for producing quality welds in manual gas tungsten arc welding (GTAW) process. In this paper, a neuro-fuzzy-based human intelligence model is constructed and implemented as an intelligent controller in automated GTAW process to maintain a consistent desired full penetration. An innovative vision system is utilized to real-time measure the specular 3D weld pool surface under strong arc light interference. Experiments are designed to produce random changes in the welding speed and voltage resulting in fluctuations in the weld pool surface. Adaptive neuro-fuzzy inference system (ANFIS) is proposed to correlate the human welder's response to the 3D weld pool surface as characterized by its width, length and convexity. Closed-loop control experiments are conducted to verify the robustness of the proposed controller. It is found that the human intelligence model can adjust the current to robustly control the process to a desired penetration state despite different initial conditions and various disturbances. A foundation is thus established to explore the mechanism and transformation of human welder's intelligence into robotic welding systems.

Original languageEnglish
Article number6589144
Pages (from-to)324-335
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Volume12
Issue number1
DOIs
StatePublished - Jan 1 2015

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

  • 3D
  • Adaptive neuro-fuzzy inference system (ANFIS)
  • human welder's behavior
  • manual gas tungsten arc welding (GTAW)
  • neuro-fuzzy modeling
  • weld pool geometry

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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