A study on the machine learning method for estimating resistance spot welding button diameter using power curve and steel type information

The automobile industry uses resistance spot welding, which is advantageous in terms of cost and productivity, for joining steel sheets the most. However, in actual field, the cost of inspection for quality evaluation is high. Therefore, research for real time prediction of the weld quality is ongoing. This study is focused on studying the button diameter prediction using artificial neural network and the power data monitored during the welding. The artificial neural network model was developed as a deep neural network model, the obtained predictions using the model are compared with the actual button diameter. As a result, a coefficient of determination of 0.99 and a root mean square error of 0.06 mm are obtained from the developed model.