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Control of the distance between the tip of an electrode wire and a base metal is important to obtain a good welding quality in spite of an arc fluctuation. Therefore, it is necessary to estimate the welding voltage relating to its distance in not only the steady state but also the transient state. For this purpose, this paper proposes neural network models which output the present welding voltage from the data relating to wire melting, such as past current, past voltage and past wire feed rate. Since performance of the neural network model depends on threshold functions, authors investigated the performance of the neural network models based on both sigmoid function and radial base function. To confirm the validity of these systems, fundamental experiments were carried out. In this paper, performances of the neural network were investigated in pulsed current welding and switch back welding the output data from the neural network were compared with the measured data. It was found that the neural network model based on the radial base function is useful than the sigmoid function to estimate the welding voltage in the switch back welding because of better responses in the transient state and smaller steady state error.

Estimation of Welding Voltage Using Neural Network in GMA Welding