A fuzzy radial basis function neural network for predicting multiple quality characteristics of plasma arc welding

We have developed an intelligent decision support system for plasma arc welding based on a fuzzy radial basis function (RBF) neural network. This approach may solve the following problems: (1) the time-consuming learning of backpropagation neural networks, (2) the fluctuation of the values of parame...

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Bibliographic Details
Published inProceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569) pp. 2807 - 2812 vol.5
Main Authors Sheng-Chai Chi, Li-Chang Hsu
Format Conference Proceeding
LanguageEnglish
Published IEEE 2001
Subjects
Backpropagation
Decision support systems
Fluctuations
Fuzzy neural networks
Fuzzy systems
Intelligent networks
Intelligent systems
Neural networks
Plasma welding
Radial basis function networks
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Summary:We have developed an intelligent decision support system for plasma arc welding based on a fuzzy radial basis function (RBF) neural network. This approach may solve the following problems: (1) the time-consuming learning of backpropagation neural networks, (2) the fluctuation of the values of parameters during welding, and (3) fuzzy linguistic-term judgment for welding quality. Based on the results obtained from Taguchi experiments, the developed fuzzy neural network can be trained to establish a quality prediction system for plasma arc welding. The developed system can also be applied to predict the welding quality for different designs of welding parameters, which are not trained. In addition, the system may support the plotting of a diagram of the 3D suitability region of the three remaining parameters when one parameter is fixed.
ISBN:9780780370784
0780370783
DOI:10.1109/NAFIPS.2001.943671