Neurofuzzy model-based predictive control of weld fusion zone geometry

Yu M. Zhang; Radovan Kovacevic

doi:10.1109/91.705507

Neurofuzzy model-based predictive control of weld fusion zone geometry

Yu M. Zhang
, Radovan Kovacevic

Research output: Contribution to journal › Article › peer-review

79 Scopus citations

Abstract

A closed-loop system is developed to control the weld fusion, which is specified by the top-side and back-side bead widths of the weld pool. Because in many applications only a top-side sensor is allowed, which is attached to and moves with the welding torch, an image processing algorithm and neurofuzzy model have been incorporated to measure and estimate the top-side and back-side bead widths based on an advanced top-side vision sensor. The welding current and speed are selected as the control variables. It is found that the correlation between any output and input depends on the value of another input. This cross coupling implies that a nonlinearity exists in the process being controlled. A neurofuzzy model is used to model this nonlinear dynamic process. Based on the dynamic fuzzy model, a predictive control system has been developed to control the welding process. Experiments confirmed that the developed control system is effective in achieving the desired fusion state despite the different disturbances.

Original language	English
Pages (from-to)	389-401
Number of pages	13
Journal	IEEE Transactions on Fuzzy Systems
Volume	6
Issue number	3
DOIs	https://doi.org/10.1109/91.705507
State	Published - 1998

Bibliographical note

Funding Information:
Manuscript received July 26, 1996; revised October 28, 1997. This work was supported by the National Science Foundation under Contract DMI-9634735, the Allison Engine Company, Indianapolis, IN, and the Center for Robotics and Manufacturing Systems, University of Kentucky, Lexington, KY.

Funding

Manuscript received July 26, 1996; revised October 28, 1997. This work was supported by the National Science Foundation under Contract DMI-9634735, the Allison Engine Company, Indianapolis, IN, and the Center for Robotics and Manufacturing Systems, University of Kentucky, Lexington, KY.

Funders	Funder number
Allison Engine Company
Center for Robotics and Manufacturing Systems
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China	DMI-9634735
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China

Keywords

Fuzzy control
Modeling
Predictive control
Welding

ASJC Scopus subject areas

Control and Systems Engineering
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

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10.1109/91.705507

Cite this

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title = "Neurofuzzy model-based predictive control of weld fusion zone geometry",

abstract = "A closed-loop system is developed to control the weld fusion, which is specified by the top-side and back-side bead widths of the weld pool. Because in many applications only a top-side sensor is allowed, which is attached to and moves with the welding torch, an image processing algorithm and neurofuzzy model have been incorporated to measure and estimate the top-side and back-side bead widths based on an advanced top-side vision sensor. The welding current and speed are selected as the control variables. It is found that the correlation between any output and input depends on the value of another input. This cross coupling implies that a nonlinearity exists in the process being controlled. A neurofuzzy model is used to model this nonlinear dynamic process. Based on the dynamic fuzzy model, a predictive control system has been developed to control the welding process. Experiments confirmed that the developed control system is effective in achieving the desired fusion state despite the different disturbances.",

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author = "Zhang, \{Yu M.\} and Radovan Kovacevic",

note = "Funding Information: Manuscript received July 26, 1996; revised October 28, 1997. This work was supported by the National Science Foundation under Contract DMI-9634735, the Allison Engine Company, Indianapolis, IN, and the Center for Robotics and Manufacturing Systems, University of Kentucky, Lexington, KY.",

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AU - Kovacevic, Radovan

N1 - Funding Information: Manuscript received July 26, 1996; revised October 28, 1997. This work was supported by the National Science Foundation under Contract DMI-9634735, the Allison Engine Company, Indianapolis, IN, and the Center for Robotics and Manufacturing Systems, University of Kentucky, Lexington, KY.

PY - 1998

Y1 - 1998

N2 - A closed-loop system is developed to control the weld fusion, which is specified by the top-side and back-side bead widths of the weld pool. Because in many applications only a top-side sensor is allowed, which is attached to and moves with the welding torch, an image processing algorithm and neurofuzzy model have been incorporated to measure and estimate the top-side and back-side bead widths based on an advanced top-side vision sensor. The welding current and speed are selected as the control variables. It is found that the correlation between any output and input depends on the value of another input. This cross coupling implies that a nonlinearity exists in the process being controlled. A neurofuzzy model is used to model this nonlinear dynamic process. Based on the dynamic fuzzy model, a predictive control system has been developed to control the welding process. Experiments confirmed that the developed control system is effective in achieving the desired fusion state despite the different disturbances.

AB - A closed-loop system is developed to control the weld fusion, which is specified by the top-side and back-side bead widths of the weld pool. Because in many applications only a top-side sensor is allowed, which is attached to and moves with the welding torch, an image processing algorithm and neurofuzzy model have been incorporated to measure and estimate the top-side and back-side bead widths based on an advanced top-side vision sensor. The welding current and speed are selected as the control variables. It is found that the correlation between any output and input depends on the value of another input. This cross coupling implies that a nonlinearity exists in the process being controlled. A neurofuzzy model is used to model this nonlinear dynamic process. Based on the dynamic fuzzy model, a predictive control system has been developed to control the welding process. Experiments confirmed that the developed control system is effective in achieving the desired fusion state despite the different disturbances.

KW - Fuzzy control

KW - Modeling

KW - Predictive control

KW - Welding

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Neurofuzzy model-based predictive control of weld fusion zone geometry

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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