Data-Driven Approach for the predictive maintenance of Resistance Spot Welding

The continuing evolution of Industry 4.0 is bringing new opportunities for companies to create value. The goal of this paper is to identify a data-driven approach for the predictive maintenance of Resistance Spot Welding (RSW), a common technique for joining sheet metals, through the real time monitoring of the electrode wear state, which is one of the most important parameters that influences the quality of a weld. The data used for the analysis was collected from sensors mounted on an RSW machine in the J-Tech laboratory at Politecnico di Torino, and then several features were extracted from it. Python and its libraries were used to perform machine learning and deep learning techniques. After the implementation of the algorithms, results have been examined in the final section, which also includes conclusions and a suggestion for future implementation.