Comparison of Machine Learning Approaches for Time-series-based Quality Monitoring of Resistance Spot Welding (RSW)

In automatic manufacturing, enormous amounts of data are generated every day. However, labeled production data useful for data analysis is difficult to acquire. Resistance spot welding (RSW), widely applied in automobile production, is a typical automatic manufacturing process with inhomogeneous data structures as well as statistical and systematic dynamics. In resistance spot welding, an electric current flows through electrodes and the materials in between. The materials are first heated and melted, then congeal, forming what is known as a weld nugget, joining the materials together. The nugget size is an important quality indicator, but can only be precisely obtained by using costly destructive methods. This paper strives to address the issue of the scarcity of labeled data by using simulation data generated with a verified finite element model. Physics-based simulation enables large amounts of labeled data to be generated with fewer limits on sensors and costs. Based on the simulation data, this paper explores and compares multiple machine learning methods, predicts the nugget size with a high degree of accuracy, and conducts an analysis of the influence of feature number and amount of training data on prediction accuracy.