Monitoring and characterization of abnormal process conditions in resistance spot welding

Resistance spot welding (RSW) is extensively used for sheet metal joining of body-in-white (BIW) structure in the automobile industry. Key parameters, such as welding current, electrode force and welding time, are involved in the RSW process. Appropriate welding parameters are vital for producing good welds; otherwise, undersized weld and expulsion are likely to be caused. For a specific type of sheet metal, an acceptable nugget is produced when an appropriate combination of welding parameters is used. However, undersized welds and expulsion are still commonly seen in the plant environment, where some abnormal process conditions could account for the production of the poor quality welds. Understanding the influence of abnormal process conditions on spot weld quality and other RSW related issues is crucial. A range of online signals, strongly related to the nugget development history, have attracted keen interest from the research community. Recent monitoring systems established the applied dynamic resistance (DR) signal, and good prediction of nugget diameter was made based on signal values. However, the DR curves with abnormal process conditions did not agree well with those under normal condition, making them less useful in detecting abnormal process conditions. More importantly, none of the existing monitoring systems have taken these abnormal process conditions into account. In addition, electrode degradation is one of the most important issues in the plant environment. Two major electrode degradation mechanisms, softening and intermetallic compound (IMC) formation, are strongly related to the characteristics of welding parameters and sheet metals. Electrode misalignment creates a very distinct temperature history of the electrode tip face, and is believed to affect the electrode degradation mechanism. Though previous studies have shown that electrode misalignment can shorten electrode life, the detailed mechanism is still not understood. In this study, an online-monitoring system based on DR curve was first established via a random forest (RF) model. The samples included individual welds on the tensile shear test sample and welds on the same sheet, considering the airgap and shunting effect. It was found that the RF model achieved a high classification accuracy between good and poor welds. However, the DR signals were affected by the shunting distance, and they displayed opposite trends against individual welds made without any shunting effect. Furthermore, a suitable online signal, electrode displacement (ED), was proposed for monitoring abnormal process conditions such as shunting, air gap and close edged welds. Related to the thermal expansion of sheet metal, ED showed good consistency of profile features and actual nugget diameters between abnormal and normal welds. Next, the influence of electrode misalignment on electrode degradation of galvannealed steel was qualitatively and quantitatively investigated. A much-reduced electrode life was found under the angular misalignment of 5°. Pitting and electrode softening were accelerated on the misaligned electrodes. δ Fe-Zn phase from the galvannealed layer that extends electrodes was found non-uniformly distributed on the worn electrode. Furthermore, electron backscatter diffraction (EBSD) analysis was implemented on the worn electrode, showing marked reduction in grain diameter and aspect ratio. The grain deformation capacity was estimated by the distribution of the Taylor factor, where the portion of pore grain was substantially weakened in the recrystallized region compared to the base metal region