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Neural Network Modeling and System Simulating for the Dynamic Process of Varied Gap Pulsed GTAW with Wire Filler

Neural Network Modeling and System Simulating for the Dynamic Process of Varied Gap Pulsed GTAW with Wire Filler
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摘要 As the base of the research work on the weld shape control during pulsed gas tungsten arc welding (GTAW) with wire filler, this paper addressed the modeling of the dynamic welding process. Topside length Lt, maximum width Wt and half-length ratio Rh1 were selected to depict topside weld pool shape, and were measured on-line by vision sensing. A dynamic neural network model was constructed to predict the usually unmeasured backside width and topside height of the weld through topside shape parameters and welding parameters. The inputs of the model were the welding parameters (peak current, pulse duty ratio, welding speed, filler rate), the joint gap, the topside pool shape parameters (Lt, Wt, and Rh1), and their history values at two former pulse, a total of 24 numbers. The validating experiment results proved that the artificial neural network (ANN) model had high precision and could be used in process control. At last, with the developed dynamic model, steady and dynamic behavior was analyzed by simulation experiments, which discovered the variation rules of weld pool shape parameters under different welding parameters, and further knew well the characteristic of the welding process. As the base of the research work on the weld shape control during pulsed gas tungsten arc welding (GTAW) with wire filler, this paper addressed the modeling of the dynamic welding process. Topside length Lt, maximum width Wt and half-length ratio Rh1 were selected to depict topside weld pool shape, and were measured on-line by vision sensing. A dynamic neural network model was constructed to predict the usually unmeasured backside width and topside height of the weld through topside shape parameters and welding parameters. The inputs of the model were the welding parameters (peak current, pulse duty ratio, welding speed, filler rate), the joint gap, the topside pool shape parameters (Lt, Wt, and Rh1), and their history values at two former pulse, a total of 24 numbers. The validating experiment results proved that the artificial neural network (ANN) model had high precision and could be used in process control. At last, with the developed dynamic model, steady and dynamic behavior was analyzed by simulation experiments, which discovered the variation rules of weld pool shape parameters under different welding parameters, and further knew well the characteristic of the welding process.
出处 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2005年第4期515-520,共6页 材料科学技术(英文版)
基金 This work was supported by the National Natural Sci-ence Foundation of China(Grant No.59635160) the Weapon Pre-Research Foundation of China(Grant No.51418050404HT0159).
关键词 Modeling Neural network Dynamic welding process Pulsed GTAW Modeling Neural network Dynamic welding process Pulsed GTAW
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  • 1Y. Gao,H.W.Hon,et al.A Large Vocabulary Mandarin Dictation System.IEEE Proc.of ICASSP’95[].Detroit MI.1995

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