# Study on Penetration Sensing Method Based on Pool Oscillation and Arc Voltage during Pulsed GMAW
## 1 Introduction  
P-GMAW (pulsed gas metal arc welding) has great advantages in the welding process and weld quality, with a splash-free transition, low heat input and good appearance of the weld bead. In the P-GMAW process, current is periodically modulated between a relatively low base current and a high peak current. By repeatly applying current pulses, a synthetic spray mode of metal transfer is primarily produced at a comparatively low level of heat input. The process has broad application prospects for automatic welding, especially for welding heat-sensitive materials and thin sheets. Weld penetration is one of most important evaluation standards that determine the quality of welding joints. Therefore, it is important to sense weld penetration during automatic P-GMAW. The weld pool dynamic behavior is a direct response to the weld pool state [1]. Therefore, the first condition to control the penetration is to extract the characteristic signal of penetration from the behavior of molten pool. In recent years, researches on the penetration sensing methods have made significant progress, which can be divided into the visual sensor [2; 3; 4], the temperature field sensor [5; 6] and the characteristic extraction of weld pool oscillation [7; 8]. Machine vision is widely used in penetration control of weld pool for which contains sufficient information to estimate weld penetration. [PERSON] et al. used a single camera to obtain two-dimensional images of the weld pool and extract the contour. The weld depth information was estimated through the outline and dimensions of the weld pool [9]. However, due to the limited information contained in 2-dimensional images, various methods for measuring the 3-dimensional surface feature information of weld pool are proposed. [PERSON] et al. reconstructedthe three-dimensional surface of the weld pool in real time in pipe GTAW (gas tungsten arc welding) process by using the structured light illuminating the weld pool surface and capturing the reflected images [10]. [PERSON] et al. proposed a method to obtain 3-dimensional features of the weld pool in GMAW by the binocular vision system. The 980 nm near-infrared filter was used to eliminate the negative interference of arc light on the weld pool image, and the solid circle array 3-dimensional reconstruction algorithm was used to reconstruct the surface morphology of the weld pool [8]. However, the reconstruction algorithm of weld pool surface is complex and time-consuming, which leads to poor real time performance of weld penetration sensing. The weld pool temperature profile can also reflect weld penetration status. [PERSON] et al. found that the relationship between the temperature gradient and penetration depth of the weld pool is inverse linear by using infrared camera [6]. Unfortunately, the welding arc and change of surface slope of weld pool seriously interfered with the measurement accuracy of the temperature field of the weld pool by the infrared camera. And these methods all require additional sensing equipment, which would increase cost and inhibits the applicability. The weld pool oscillation is one of the feature signals for characterizing penetration status in GTAW. The oscillation frequency had an inverse linear relationship with the penetration status of weld joints, and a cliff descent occurs in oscillation frequency from partial to full penetration in stationary and low welding speed due to different boundary conditions of the pool in GTAW [11]. [PERSON] et al. retrieved the weld pool oscillation frequency in P-GTAW by using the reflected image of structured light which illuminated on the weld pool surface, and similar results were obtained [7]. [PERSON] et al. studied the relationship between voltage signal and the oscillation of the weld pool in P-GTAW, and it was found that the frequency and the amplitude of the weld pool oscillation can be exacted from voltage signal [12].