Development of real-time diagnosis framework for angular misalignment of robot spot-welding system based on machine learning

This paper focuses on the real-time online monitoring and diagnosis framework for the angular misalignment of the robot spot-welding system, which can result in significant quality degradation of a weld nugget such as porosity. The data-driven approach is applied by installing the voltage and current sensors, collecting the associated mass data and processing them under normal and abnormal (angular misalignment) conditions. Two categories of features are extracted from the dynamic resistance (DR) and the voltage and current ones that are decomposed by wavelet transform (WT). The DR features are extracted from the DR profile and some critical features are selected by a t-test methodology. In the case of the WT-based features, the critical ones are selected by a max-relevance and min-redundancy (mRMR) and a sequential backward selection (SBS) wrapper. Consequently, three types of critical feature sets, such as DR features, WT features, and hybrid features combining those, are prepared to train machine learning-based models. Support vector machine (SVM) and probabilistic neural network (PNN) are applied to establish the diagnosis models, and the diagnostic accuracy and robustness are evaluated. Finally, the software for the on-line monitoring and diagnosis for angular misalignment of robot spot-welding system is developed and demonstrates its real-time applicability in an industrial site.