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  HomeContents of Chinese Journal of Mechanical Engineering (English Edition),2002 No.1ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL
ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING

AND CONTROL*

 

Gao Xiangdong
Faculty of Mechanical and Electrical Engineering, Guangdong University of Technology,
Guangzhou 510090, China

Huang Shisheng

South China University of Technology

 

Abstract: An artificial neural network (ANN) and a self-adjusting fuzzy logic controller (FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

Key words: Artificial neural network  Fuzzy logic control  Weld pool depth  Seam tracking


* This Project is supported by National Natural Science Foundation of China and Provincial Natural Science Foundation of Guangdong, China. Received February 12, 2001; received in revised form July 20, 2001; accepted September 6, 2001

 

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