Estimation of Welding Current with Adaptive Neuro Fuzzy Inference System (ANFIS): Utilization of Arc Light Signal Emitted in the Arc Welding Process

The main purpose of this study is to estimate the welding current using the arc light signal emitted during the welding process. Traditionally, welding operators determine this current from the arc light based on their visual perception. This study shows that, using artificial intelligence techniques, welding current can be automatically estimated through arc light and can also be useful for monitoring of the process and detecting its disturbances. For this purpose, initially, a data acquisition system is designed to synchronize the movement of the light sensor with the electrode holder. The electrode welding machine is set to different maximum current levels, and two electrodes with different diameters are used at each level. During the welding process, the arc light and current signals are acquired simultaneously. The obtained data are filtered and aligned by cross-correlation. For the ANFIS (adaptive neuro-fuzzy inference system) model, the arc light is defined as the input and the current as the output. The estimation results of ANFIS are further improved through filtering, shifting, and current-limiting processes. The maximum cross-correlation values for training and testing data are 0.9587, 0.9598, 0.9565, and 0.9323, respectively, while the R-squared values are 0.7033, 0.7640, 0.6449, and 0.5853. Compared with the artificial neural network (ANN) model, it is observed that the ANFIS model provides better prediction results. The results confirm that arc light signals can be effectively used for welding current prediction. Therefore, the proposed approach can contribute to the development of intelligent welding systems and quality welding processes by reducing operator dependency.