## 1 Introduction
Among artificial intelligence methods, the neuro-fuzzy approach stands out as a hybrid system formed by combining neural networks and fuzzy logic [28]. Based on this approach, [PERSON] [29] developed the ANFIS architecture using a hybrid learning procedure. In particular, the ability of ANFIS to model nonlinear functions in welding processes makes it a powerful tool in the field of parameter estimation [28]. ANFIS has been used as an effective method for analyzing the effects of welding parameters, making real-time predictions and automatically extracting weld seams [30; 31]. In addition, ANFIS can be effectively used in predicting weld quality and in determining metal deposition and seam width [32; 33; 34; 35]. Furthermore, ANFIS has been successfully applied in various fields such as analyzing the responses of human welders, estimating penetration, understanding surface reactions, and developing intelligent welding machines [36; 37; 38; 39]. Another alternative artificial intelligence method frequently used in welding processes is ANN. [PERSON] et al. [40] developed a neural network (NN) arc sensor that estimates the electrode wire and arc lengths using voltage and current measurements. In addition, ANN models have been effectively used to determine various weld properties such as quality, location, optimal working point, seam width, penetration depth, seam geometry, and the joining of dissimilar metals [41; 42; 43; 44; 45; 46]. In recent studies, the prediction results of ANFIS and ANN models are directly or indirectly compared. [PERSON] et al. [47] estimated the average power consumption of an electrode welding machine using ANFIS with noise characteristics and compared this result with ANN. Performance comparisons of ANFIS and ANN models in estimating weld seam width and penetration depth were made [48; 49]. In the study conducted by [PERSON] et al. [50], ANFIS and ANN models were also compared according to regression analysis to estimate weld properties such as dilution and weld seam geometry, and the results were evaluated according to root mean square error (RMSE). There are also studies in which different methods such as ANFIS and MGGP were examined comparatively in estimating welding processes [51]. These studies show that both ANFIS and ANN are effective methods for controlling, predicting, and optimizing the welding process. In this framework, although the studies in the literature have presented various approaches for light and arc welding parameters of the welding process in artificial intelligence, the studies on the light and current estimations are limited. At the same time, current estimation with ANFIS light input is a subject that has not been fully elucidated in the current literature. This method aims to make current estimation with light signals, which has not been sufficiently studied in the scientific literature before. The results provide important findings on current estimation with arc light signals and contribute to research in this field. In conclusion, the integration of such artificial intelligence approaches into welding processes has the potential to contribute to increased efficiency, safety, and sustainability, not only in specific areas such as current estimation but also in broader areas such as improving weld quality, parameter optimization, and energy efficiency. Furthermore, it demonstrates that it can be useful for monitoring the course of the process, detecting disturbances and anomalies.