Abstract: The aim of this study is to perform an analysis of the different ANN methods employed to detect the saturation level in the magnetic core of a welding transformer. The magnetization level detector is a substantial component of a middle-frequency direct current (MFDC) resistance spot welding system (RSWS). The basic circuit of a resistance spot welding system consists of an input rectifier, an inverter, a welding transformer, and a full-wave rectifier which is mounted on the output of the welding transformer. The presence of unbalanced resistances of the transformer secondary windings and the difference in characteristics of output rectifier diodes can cause the transformers magnetic core to become saturated. This produces spikes in the primary current and finally leads to the over-current protection switch-off of the entire system. To prevent the occurrence of such a phenomena, the welding system control must detect and maintain the magnetic core saturation within certain limits. Previously, an Artificial Neural Network based detector was proposed to detect the saturation level. In this paper, we will take an in depth look at the different ANN methods that can be employed, analyze them and then decide which is the best method based on factors such as computational time, algorithm complexity, root mean square error and the gradient obtained. Three algorithms in total are evaluated including Resilient-Back propagation, Levenberg-Marquardt, Gradient-Descent. Based on the results obtained, the Levenberg-Marquardt and the Resilient Back propagation were found to be the best algorithms but the Resilient Back propagation is preferred due to reasons such as computational time and algorithm complexity. In terms of RMSE both gave almost the same values. It is also shown that the Gradient Descent algorithm cannot be employed for this purpose.
Keywords: Transformers, hysteresis, welding, neural network applications, controllers, gradient descent algorithm, levenberg-marquardt algorithm, resilient back propagation algorithm, training methods.
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