D. Fan
,
B. Li
,
Y.Z. Ma and J.H. Chen (Welding Institute
,
Gansu University of Technology
,
Lanzhou 730050
,
China)
金属学报(英文版)
In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.
关键词:
welding spatter
,
null
,
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