# Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions
## 2 Related Works
Their study showed that the best results were obtained when optical (photodiodes) and electrical (current and voltage) process data were used together [12]. [PERSON] et al. [13] also used artificial neural networks to determine the mechanical properties of high-strength steels. Their study generated yield and tensile strength data concerning welding parameters and a filler wire with a similar carbon equivalent. They stressed the importance of considering additional factors, such as filler wire characteristics, to accurately predict the mechanical properties of solder joints. [PERSON] et al. focused on burn-through prediction and weld depth estimation in GMAW [14]. The variation in overpenetration and burn-through among samples was explained in the study by the inevitable influence of disturbances during the welding process and the instability of the welding arc. They developed a deep convolutional neural network model to assess both situations satisfactorily. Designing a static feature pattern that allows consistent detection of the weld pool boundary or position under different welding conditions is relatively complicated. [PERSON] et al. [15] also used deep neural networks for this purpose. Their study aimed to automatically track the weld pool boundary to monitor the process and collect operational and process data for operator analysis and modeling. In their case study, intense arc radiation and unpredictable operation presented additional challenges, which were overcome using U-Net architectures. [PERSON] et al. [16] also used deep neural networks to improve welding. Their research focused on identifying and categorizing weld current. They tested several architectures (in alphabetical order: DarkNet53 [17], DenseNet201 [18], EfficientNetB0 [19], InceptionV3 [20], MobilenetV2 [21], NASNetLarge [22], ResNet18 [23], ResNet101 [23], and Xception [24]) for weld current detection and classification, with good results. In addition, [PERSON] and [PERSON] designed lightweight networks for automatic weld seam tracking [25]. They use denoising and object detection, followed by information-driven structural pruning with Bayesian Optimization. [PERSON] et al. also focused on a machine vision-based algorithm for robotic weld path detection, gap measurement, and weld length calculation in the GMAW process. They used a low-cost webcam camera located on the robotic arm to capture images, which were later converted to grayscale. In addition, YOLOv5 [26] was used for identifying atypical weld regions [5]. [PERSON] et al. leveraged Generative Adversarial Networks combined with Gated Recurrent Units in a human-robot collaborative system for GMAW [27]. Their system used time-series torch movement data and corresponding weld pool images. From a different perspective, [PERSON] et al. [28] pioneered using fuzzy sets for GMAW analysis. Using measured welding voltage and current signals, they developed a fuzzy logic system capable of recognizing common disturbances during automatic gas metal arc welding (GMAW). The proposed system could recognize and classify disturbed and undisturbed GMAW experiments using only the measured welding voltage and current signals. [PERSON] et al. also employed fuzzy systems for assessing the geometries and quality of weld beads [29]. They emphasize the uncertainties of the semi-automated GMAW process and leverage on soft computing to obtain satisfactory results. The prediction model proposed in that study considered parameters such as wire feed speed, travel speed, contact tip-to-work distance, and electrode working angle. In addition, [PERSON] et al. [30] applied fuzzy logic to GMAW quality assessment. They proposed a fuzzy model that uses fuzzy expert rules, triangular membership functions, and the centroid area method to obtain effective defuzzification. This enabled them to predict acceptable parameters for robotic gas metal arc welding, following the quality standards set out in AWS B4.0:2016 and BS EN ISO 5817:2014. [PERSON] et al.