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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. 

Abstract Laser keyhole welding of dissimilar metals has been widely used in industrial applications. One critical challenge for this process is the formation of intermetallic compounds (IMCs) that undermine the electrical and mechanical properties of the joints. Compared with the commonly used linear contours, welding with spiral contours can provide larger areas of joining and hence higher allowable loading. This can be particularly useful for certain applications. In this research, laser welding experiments with different spiral contours were performed, and the chemical composition, microstructure, and mechanical properties of the joints were characterized. Three spiral distances were used in the experiments. As the spiral distance was changed from 0.1 mm to 0.3 mm and 0.5 mm, the average Cu concentration in the upper region of the joints was decreased, lower amounts of IMCs were found in the joints, and the joints were capable of sustaining higher mechanical loading. 

Abstract Friction surfacing is a new variation of friction stir processing for surface property modification of metallic substrates. There is an increasing body of literature about friction surfacing by deposition of metal from a consumable tool to a solid substrate. Friction surfacing has many potential applications in joining, coating for corrosion resistance, and repair of degraded components. This article presents a review of the basic principles and latest research organized by processing techniques and variations, thermomechanical transfer and deposition of material, and finally metallurgical, mechanical, and chemical properties of the resulting deposition. Different friction surfacing processes are reviewed of novel tool–substrate configurations for material deposition for noncoating purposes like keyhole filling and joining dissimilar materials. Possible future topics of study for this area are discussed, which include deeper understanding of material transfer through metallurgy, FEM, and scale up of the technique for practical application.