# System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam
## 1 Introduction  
Crucial investigation regarding robot-based automation are required in modern industrial manufacturing, such as aerospace assembly, high-speed railway construction, nuclear power plants development, and other various applications [1]. When producing large and complex aluminum workpieces under high-quality needed conditions, the friction stir welding method is often applied due to its advantages of easy performing and materials protection [2]. In such method, the welding wire is not required, and damages like the arc light are avoided. However, in order to refine the aerodynamic performance of the workpieces, the weld seam needs to be appropriately ground as glitches are often occured at the edge of the welding area. The traditional manual grinding method's accuracy largely depends on the experience of operators. Manual grinding makes it difficult to produce qualified workpieces, and grinding is dangerous and harmful to the operators. Therefore, the robotic grinding system is suitable for friction stir welding because it is safer and more efficient. In the grinding, it needs to be ensured that the welding area is free of the oxide film, possesses metallic luster, and the grinding lines are parallel to the weld direction. At present, although the removal of friction stir weld seam can adopt milling, when processing lightweight and thin-walled components, the workpieces are easily deformed and have poor rigidity, so it is inappropriate to use a milling method with position control and large cutting force. In terms of grinding tools, belt grinding is more suitable than other tools like angle grinder or grindstone for two crucial reasons. On the one hand, the belt grinding is flexible and referred to as\"cold grinding\", which means that the heat is largely reduced during the grinding process. On the other hand, the grinding is easier to control when compared to traditional tools. However, in practical industrial applications, an excessive grinding shall damage the base metal and cause workpieces to be obsoleted, which increases the cost of manufacturing. Generally, the grinding depth should not exceed 10% of the thickness of the base metal [3]. Moreover, the robot grinding process is dynamic and full of uncertainty. Therefore, to avoid such conditions, an online monitoring method needs to be developed. The grinding procedure should immediately stop when there exists an excessive grinding. Recently, there are many types of researches on robot grinding and monitoring methods. In the area of robot grinding research, much of the work involves controlling the grinding process and the prediction of the grinding depth. Many belt grinding systems and control methods have been presented in recent decades [4; 5; 6]. [PERSON] et al. used a statistical machine learning method to build an adaptive grinding model and predict the grinding depth [7; 8]. Predictive modeling and analysis of process parameters on material removal characteristics are conducted in [9]. Refs. [10; 11; 12; 13; 14; 15] focused on trajectory and vibration research of the belt grinding. [PERSON] et al. described a novel methodology to bridge the knowledge transfer gap between the manual operator's skills and a robot, and Key Process Variables (KPVs) were designed based on experiences [16]. However, most of the current researches are toward the low-curvature surface workpiece rather than the weld seam. [PERSON] et al. proposed a novel detecting and grinding robot for weld seam in pipe [17], but the proposed system is only for pipelines. In the area of robot grinding monitor research, some researchers investigate acoustic signal methods to detect damages in the grinding process [18; 19; 20]. [PERSON] et al. proposed a multi-sensor fusion approach to improve monitor quality [21]. Other parameters like heterogeneous data [22], temperatures [23], and surface roughness [24] have been researched in grinding. Ref. [25] proposed a belt grinding system with a constant force control module, which used motor current and the adjustment distance for monitoring.