# Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions
###### Abstract  
In the industry, the robotic gas metal arc welding (GMAW) process has a huge range of applications, including in the automotive sector, construction companies, the shipping industry, and many more. Automatic quality inspection in robotic welding is crucial because it ensures the uniformity, strength, and safety of welded joints without the need for constant human intervention. Detecting defects in real time prevents defective products from reaching advanced production stages, reducing reprocessing costs. In addition, the use of materials is optimized by avoiding defective welds that require rework, contributing to cleaner production. This paper presents a novel dataset of robot GMAW images for experimental purposes, including human-expert segmentation and human knowledge labeling regarding the different errors that may appear in welding. In addition, it tests an automatic segmentation approach for robot GMAW quality assessment. The results presented confirm that automatic segmentation is comparable to human segmentation, guaranteeing a correct welding quality assessment to provide feedback on the robot welding process. c 2025 15 3280 2025 13 March  
The robotic GMAW process has a wide range of applications in the industry, including the automotive sector, construction companies, the shipping industry, and many more. Nowadays, with modern manufacturing and Industry 4.0 applications, industries need to monitor the welding quality, waste acquisition, and contamination and even decrease the process costs, making it more economical [3]. The fundamental problem is to monitor the robot's correct application of the weld bead based on many input variables such as output current, pulse time, voltage, wire feed speed, geometry profile, and robot path [1]. In this process, toxic chemical reactions occur that become environmental pollution when the steel is exposed to high temperatures caused by electricity and the reaction with argon and carbon dioxide in the shielding gas. Every minute used in a welded part equals one minute of environmental footprint and energy consumed. In addition, when parts are manufactured in a serial process, the operation time is precise from the start of the welding process until the welding seam is made. Therefore, it is logical to ensure that the inspection activity time is not greater than the operation time [4]. Taking this period as a reference, different techniques for inspecting the parts exist. Currently, a specialist performs the quality assessment process as automated inspection has evolved and increased in the development and implementation of that process. One of these quality control methods is to take photographs and analyze them, such as the geometry, color, and path of the welded parts. The main goal is to replace the specialist with an artificial intelligence method that uses an inexpensive camera and free software [5]. Recently, several methods of automatic robotic weld seam analysis have been developed. There are numerous differences in how to acquire the weld image, what features would be analyzed in the image, and what type of image processing algorithm has better results. The main decision in applying a specific model depends on the image quality. In this sense, there are high-cost cameras made to withstand the high temperatures inherent to the welding process and low-cost cameras, such as a webcam, which must be far from the robot that executes the welding process and whose technology is not robust enough. Another difference is the use of image processing techniques. In some works, specialized software (usually with the camera) is used, and only segments must be identified. In some researches, free software has been used, where it is necessary to use filters to clean the image before identifying the features of interest [3; 5]. Research has considered different cameras for the welding process and certain additional elements such as the welding electrode's distance, width, area, and angle.