Abstract
Resistance Spot Welding (RSW) is one of the largest automated manufacturing processes in industry, consequently making it also one of the most researched. While this ubiquity has led to advancements in the consistency of this process, RSW is innately uncertain due to the high degree of interplaying mechanics that occur during the process. Additionally, to ensure the quality of a completed weld empirically, expensive analysis tools are required to inspect the result. One solution to removing this monetary and temporal cost is in-line process monitoring. During the weld, various signals can be measured and evaluated to predict the weld quality in real-time. The most common signal to measure is the Dynamic Resistance (DR) due to its ease of sensor implementation and richness of information. Other common signals are the electrode force and displacement. These give a more inclusive look into the overall process, especially the mechanical aspects, but these are typically limited to lab settings due to the increased cost of deploying them at scale. One solution to realize the insight of these other process signals on the factory floor is to utilize Machine Learning techniques to create virtual sensors that convert extant sensing data to other domains. This would allow for more robust and interpretable signal processing without incurring additional costs or downtime.
