| ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL |
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| 作者姓名： | Gao Xiangdong Faculty of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou China Huang Shisheng South China University of Technology |
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| 作者单位： | Gao Xiangdong Faculty of Mechanical and Electrical Engineering，Guangdong University of Technology， Guangzhou 510090，China Huang Shisheng South China University of Technology |
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| 基金项目： | National Natural Science Foundation of China and Provincial Natural Science Foundafion of Guangdong, China. |
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| 摘 要： | 0 INTRODUCTION(The satisfied control of the overall weld process is not easily accomplished, largely due to the inadequacies of the available process models. Without exceptions, most welding control methods are based upon the analytical welding models. Although these models are derived directly from the physical laws that govern the main features of the weld pool, a number of assumptions are made to obtain the mathematical solutions and some variables are ignored due to the complexity of t…
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ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL |
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| Gao Xiangdong Faculty of Mechanical and Electrical Engineering,Guangdong University of Technology, Guangzhou ,China Huang Shisheng South China University of Technology.ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL[J].Chinese Journal of Mechanical Engineering,2002,15(1):53-56. |
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| Authors: | Gao Xiangdong Huang Shisheng |
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| Affiliation: | Faculty of Mechanical and Electrical Engineering,Guangdong University of Technology,Guangzhou 510090,China South China University of Technology |
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| 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. |
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| Keywords: | Artificial neural network Fuzzy logic control Weld pool depth Seam tracking |
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