A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier

To develop an effective nondestructive evaluation method for the welding quality of the resistance spot welding, the electrode displacement signal during the resistance spot welding process is monitored, and the acquisition data of the signal are innovatively presented as the radar chart format. Some geometric features of the radar charts are extracted to reflect the welding quality. The decision tree classification technique is adopted to build a classifier and to provide a visible and intuitive diagnostic procedure for welding quality assessment. Test results of the decision tree classifier show that it is feasible and reliable to evaluate weld quality based on the graphics features of the radar chart. The features and the weld quality are closely related, and their extraction avoids complex algorithm. Meanwhile, when there are small samples, the decision tree classifier can identify good or bad weld accurately and rapidly, even though the weld is from an abnormal welding process, such as expulsion, current shunting, greasy surface, and small edge distance condition.