## 1 Introduction  
Arc welding processes have great importance in industrial applications, and parameter estimation plays a critical role in welding automation. Arc light, one of the welding parameters, is a fundamental phenomenon associated with the arc welding process, and its emission provides valuable information about the welding process [1]. The spectral distribution and visible radiation of the arc light are used as an important indicator for monitoring the welding process and evaluating its quality [2, 3, 4, 5]. Various techniques have been developed to obtain information from the arc light, and visual perception has the advantage of providing this information without requiring physical contact [6]. These techniques are divided into two categories as active and passive in the context of visual perception systems and are effectively used in the welding process [7]. In passive vision, the object is directly viewed, while in active vision, laser or structured light is reflected onto the object surface. Active and passive visual sensors (camera, luxmeter, laser vision systems (LVS), structured light sensors, etc.) are used together with artificial intelligence integration and intelligent welding robots to provide effective results in the welding process. These sensors play an important role in areas such as preventing welding noise, process control, and quality improvement [7; 8; 9; 10; 11]. For example, photodiodes and light intensity measurements have been used in the detection of the properties of weld melt pools and in obtaining shape information [6; 12]. In addition to visual detection in welding processes, arc, acoustic, and other detection techniques (touch, electromagnetic, infrared, temperature, ultrasonic, radiographic) are widely used [13; 14; 15; 16]. Studies in the literature show that the combined use of different detection methods in welding processes provides significant advantages in terms of process monitoring, algorithm development, fault detection, quality control, fusion algorithm, and methodology development [17; 18; 19; 20]. Arc light is also closely related to various welding parameters. It has been determined that there is a functional relationship between arc light intensity and arc length [21] and between arc brightness and current [22]. In this context, to analyze the parameters related to arc light more accurately, it is necessary to eliminate unwanted data in the acquired signals. In this direction, filtering is a widely used method. Physical optical and digital filters are frequently preferred. In the study of [PERSON] et al. [22], arc brightness was measured with a luxmeter and protected using a level 6 welding mask light filter (physical optical filter). In order to obtain a more accurate digital signal by filtering unwanted frequency components, [PERSON] et al. [23] passed the arc voltage and current signals through Butterworth low-pass filters (digital filters) before entering the data acquisition board (DAQ board). In addition, in another study by [PERSON] et al. [24], a Gaussian low-pass filter (digital filter) was applied to the arc signal obtained from the welding arc sensor. The acquired and filtered signals are effectively used in control systems for parameter estimation. Closed-loop control systems developed using arc light [25] and welding current determination models proposed by [PERSON] et al. [26] are important examples for the optimization of this process. In this context, effective processing and interpretation of data obtained through process control enables the development of more intelligent and adaptable systems. Recently, machine learning and artificial intelligence techniques have become increasingly important in terms of creating data-driven decision mechanisms and parameter estimation in welding processes. Machine learning is an artificial intelligence application that enables systems to learn through experience and plays an important role in increasing efficiency in welding processes [27].