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A Study of the Application of Neural Network for the Prediction of Top-bead Height  

Son, J.S. ((주)프로맥스 기술연구소)
Kim, I.S. (목포대학교 기계선박해양공학부)
Park, C.E. (목포대학교 기계선박해양공학부)
Kim, I.J. (한국생산기술연구원)
Kim, H.H. (목포대학교 대학원 기계공학과)
Seo, J.H. (목포대학교 대학원 기계공학과)
Shim, J.Y. (목포대학교 대학원 기계공학과)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.16, no.4, 2007 , pp. 87-92 More about this Journal
Abstract
The full automation welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed models using three different training algorithms in order to select an adequate neural network model for prediction of top-bead height.
Keywords
Neural network; Radial basis function network; Robotic arc welding; Top-bead height; Process parameter;
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