# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 1 Introduction  
Arc welding is a widely used process for joining various metals. The electric current is transferred from the electrode to the work piece through the arc plasma, which has been modeled to control the weld quality in the past research [1; 2]. However, the direct factor that affects the weld quality is the geometry of the weld pool instead of arc plasma because the skilled welders achieve good weld quality mainly based on the visual information of the weld pool. During arc welding, the incomplete weld pool penetration will reduce the effective working cross-sectional area of the weld bead and subsequently reduce the weld joint strength. It also causes stress concentrations in some cases, e.g., the fillet and T-joints. On the contrary, excessive weld pool penetration might cause melt-through. The skilled welder needs to adjust the position and travelling speed of the weld torch based on the information of the observed weld pool surface to achieve complete penetration. The shortage of skilled welders and a need for welds of a consistently high quality fuels an increasing demand for automated arc welding systems. It is believed that machine vision techniques will lead the development of the next generation intelligent automated arc welding systems. In recent years, great efforts have been put to develop the automated and high-precision welding equipment to achieve high quality welded joints consistently. The important parts of this kind of automated welding equipment include the seam tracking system [3; 4; 5; 6], the weld penetration monitoring system [7; 8; 9] and the control system. Desirably, the seam tracking system or the monitoring system and control system are in a closed loop. Thus, the tracking result or the monitoring result could serve as feedback for the control system to ensure high quality of welding. In the past studies, it has been shown that weld pool surface depression has a major effect on weld penetration [10; 11; 12; 13; 14]. The geometry of the weld pool surface will affect the convection in the pool. The primary welding parameter, the welding current density is also affected by the geometry of the weld pool greatly. In return, the plasma and the welding current affect the geometry of the weld pool. Both the arc plasma and the molten weld pool are affected by the current density distribution. Therefore, it is fundamental for the automated welding systems to take quantitative measurements of the weld pool surface. In the past decades, a lot of research has been conducted to measure the shape of the arc welding weld pool by structured light methods [9; 15; 16; 17; 18; 19; 20; 21; 22; 23]. In Reference [15], the authors pioneered to measure the deformation of the weld pool by inventing a novel sensing system. Their sensing system projects a short duration pulsed laser light through a frosted glass with a grid onto the specular weld pool. The reflected laser stripes are imaged in a CCD camera and the geometry of the reflected stripes contain the weld pool surface information. This method might be the earliest method that made good use of the reflective property of the weld pool surface and achieved state of the art accuracy at that time. In Reference [16], the authors improved the structured light method for gas tungsten arc weld (GTAW) pool shape measurement by reflecting the structured light onto an imaging plane instead of onto the image plane of the camera directly. The imaging plane is placed at a properly selected distance. Thus, the imaged laser patterns are clear enough while the effect of the arc plasma has attenuated significantly, because the propagation of the laser light is much longer than that of the arc plasma. Ever since then, this weld pool sensing technique has become the mainstream of weld pool imaging technology [17; 18; 19; 20; 21; 22; 23]. In References [17; 18], the authors tried to increase the accuracy of measuring the 3D shape of the GTAW weld pool sensed by the same imaging system as [16].