# Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions
## 2 Related Works  
This section presents several related works in the field of automatic welding quality assessment that justify the relevance of this topic and provide a better description of the background in the field. It is important to note that, since 1968, research papers have focused on using computers to improve welding quality assessment [7]. [PERSON] et al. [4] reviewed the use of machine learning models in metallurgic and other industries. Their results showed the adaptability and potential applicability of artificial intelligence techniques to almost any stage of the process of any product due to the use of complementary techniques that adapt to the different particularities of the data, production processes, and quality requirements. Automatic weld quality detection is a growing research topic due to its direct impact on the safety, reliability, and efficiency of various industries, such as automotive, aerospace, shipbuilding, and infrastructure manufacturing. This field of research is of great importance for several reasons, including the following:  
1. Safety: Defective welds can lead to severe structural failures in products or infrastructure, posing a significant risk to human safety. Detecting these defects early can prevent accidents and human loss. 2. Efficiency: Inspecting welds manually is a process that can be slow, expensive, and prone to human error. Automatic systems can speed up inspection and allow more quality tests to be performed in less time, improving productivity and reducing manufacturing costs. Quality and Consistency: Weld quality can vary depending on factors such as welder technique, environmental conditions, and material quality. Automatic detection methods can provide a more accurate and consistent weld quality assessment compared to traditional visual inspections. * Cost Reduction: Automatic detection can reduce costs associated with repairing defective welds and labor costs for inspection. It also improves traceability and quality management throughout the manufacturing process. * Defect Complexity: Welds can have very complex defects that are not always visible to the naked eye, and their exact location can be challenging to determine. Detecting them using automatic technologies, such as ultrasound, X-ray, or computer vision, requires advanced approaches to ensure that potential flaws are not overlooked. Several studies related to the use of machine learning algorithms to improve welding processes are reviewed below. GMAW welding is being studied for its wide use in industry and the mechanical properties it provides due to its high productivity and processing speed, such as automotive and heavy manufacturing equipment [8]. Its automatic quality control goes back to 1980 [9]. [PERSON] et al. [10] investigated using Artificial Neural Networks (ANN) to develop an adaptive GMAW parameter control system. Their study combined a computer vision system with an artificial neural network-based welding parameter control system to achieve a more consistent welding result and higher weld quality under varying welding conditions. The study further enabled the development of an ANN-controlled robotic welding system for multi-pass butt welding on a 12 mm S420 MC plate without root support. It was also established how the developed ANN system adapted to varying welding conditions and pit welds in a robotic welding application. Their later experiments [11] included using the Levenberg-Marquardt Algorithm (LMA) for ANN training and incorporating a welding adaptive feedback control system for welding process parameters in a laboratory environment. [PERSON] et al. again focused on the classification of different deviations of the gas metal arc welding process using artificial neural networks [12]. To do so, they used transient optical and electrical data of the welding process. The deviations examined ranged from large process disturbances due to insufficient shielding gas coverage to a slight shift in the process operating point due to a poorly positioned welding torch.