# Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding
## 1 Introduction  
Keyhole tungsten inert gas (K-TIG) welding is a promising welding technology for deep penetration of medium and thick plate materials [1]. Developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Canberra, Australia) of the Australian government in 1997, K-TIG welding utilizes a high current to generate an arc with high energy, strong penetration force, and high stiffness, enabling it to weld metal materials directly without the need for grooving or filler wire [2]. By achieving single-side welding and double-side forming in one step, K-TIG welding significantly enhances welding efficiency while reducing welding costs [3, 4]. Nonetheless, given the characteristic of K-TIG welding that achieves single-side welding and double-side forming in one step, ensuring its quality during the welding process is paramount. The success of keyhole tungsten inert gas (K-TIG) welding relies heavily on the stability of the molten pool keyhole during the welding process. This stability is determined by a delicate balance between the arc pressure, the liquid surface of the back small hole, and the gravity of the metal [5]. The welding process is complex and characterized by non-equilibrium and instantaneous features. To ensure the stability and reliability of welding quality, it is essential to employ online welding quality evaluation technology [6, 7, 8]. This technology not only improves welding efficiency and productivity but also reduces the need for offline destructive or non-destructive testing methods. Current mainstream signal acquisition methods for online welding quality evaluation involve sound [9, 10, 11], spectral [12, 13], current-voltage [14, 15, 16, 17], and visual sensing methods [18, 19, 20]. However, the strong arc light generated by the high current (300-1000 A) of K-TIG welding makes it difficult to evaluate welding quality using visual methods. At the same time, practical limitations, such as the cost of visual acquisition equipment andspace requirements for welding platforms, make visual sensing methods challenging to implement [21]. As an alternative, acoustic sensing has been proposed due to its lower cost and easier installation. However, sound noise generated in actual factory production processes can make it difficult to denoise and obtain a pure arc sound [22]. In comparison, the arc electrical signal is closely linked to the welding process and is relatively easier to acquire. Therefore, this paper chooses to evaluate welding quality through the arc electrical signal. A novel method based on the arc electrical signal is proposed for online welding quality evaluation in K-TIG welding. The method involves collecting the arc electrical signal using a high-speed data acquisition system, followed by filtering and denoising the signal to remove unwanted noise. Subsequently, advanced signal processing techniques, including feature extraction and classification algorithms, are employed to analyze the signal and evaluate welding quality in real time. Experimental results demonstrate that the proposed method achieves high accuracy in evaluating welding quality in K-TIG welding, even in the presence of strong arc light interference; furthermore, this method has the advantages of low cost and easy installation compared to visual and acoustic sensing methods [23; 24]. Therefore, the proposed method based on the arc electrical signal shows great potential for online welding quality evaluation in K-TIG welding applications. Arc electrical signal sensing encompasses arc current, arc voltage, and plasma voltage discharged from the back. In order to enhance the penetrating ability and welding quality of keyhole plasma arc welding, a novel penetration closed-loop control system has been established by [PERSON] et al. [23]. This system chooses the welding current and plasma gas flow rate as the adjustable variables. Real-time detection of penetration status is achieved from the welding arc voltage using the wavelet method.