신경회로망을 이용한 이면비드 폭 예측에 관한 연구

Abstract

The development of robotic welding process is a very complex assignment because the system is affected by a number of process parameters which are very difficult to determine or predict in practice. 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, micro structure and weld properties in arc welding processed 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 specific experimental result. 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 back-bead width as a function of key process parameters in the GMA (Gas Metal Arc) welding and to compare the developed models with the experimental results.