This research presents a novel, low-cost optical acquisition system based on infrared imaging for real-time weld bead geometry monitoring in Gas Metal Arc Welding (GMAW). The system uniquely employs a commercial CCD camera (1000–1150 nm) with tailored filters and lenses to isolate molten pool thermal radiation while mitigating arc interference. A single camera and a mirror-based setup simultaneously capture weld bead width and reinforcement. Acquired images are processed in real time (10 ms intervals) using MATLAB R2016b algorithms for edge segmentation and geometric parameter extraction. Dimensional accuracy under different welding parameters was ensured through camera calibration modeling. Validation across 35 experimental trials (over 6000 datapoints) using laser profilometry and manual measurements showed errors below 1%. The resulting dataset successfully trained a Support Vector Machine, highlighting the system’s potential for smart manufacturing and predictive modeling. This study demonstrates the viability of high-precision, low-cost weld monitoring for enhanced real-time control and automation in welding applications.

Gas metal arc welding (GMAW) processes need to guarantee the quality of the parts produced from this stability in the manufacture of a single bead. In addition to the visual inspection and subsequent characterization with the cutting of the samples, which consists of destructive analysis, it is possible to monitor the entire process and the quality of the part in a non-destructive way. Therefore, this work aims to develop a methodology for the analysis of non-destructive and online weld beads using high-speed cameras, thermal cameras, profilometer and algorithms in MatLab for data processing. The high-speed cameras allow the capture of images of the electric arc and the metallic transfer mode. The thermal cameras, on the other hand, allow the visualization of the melt pool and the temperatures reached during the deposition. Finally, the laser profilometer allows you to make a point cloud of the part and measure its geometry (height, width, height-to-width ratio, wetting angle) online and more accurately than the caliper. With this, it is possible to use the data, both for geometric quality, preliminary parameterization of additive manufacturing, and later for feeding simulations of welding. Promising the monitoring process during manufacturing.

One of the main problems of additive manufacturing with electric arc and welding, in general, is the difficulty in controlling or predicting the output variables and their parameters, as well as creating a model that effectively represents the changes in the main variables involved in the system. These changes during the deposition process can promote the formation of splashes, instabilities, and changes in the geometry of the beads, making the analysis of these variables important, as it will be through them that the quality of the deposit and the desired characteristics will be established. Despite the correlation between the variables, they present nonlinear and chaotic behavior. With this, the purpose of this research is mathematical modeling in state space that allows an approximation to the model in state spaces, an approximation of the real values of the process, and a knowledge of the system composed of a set of input, output, and states related to each other by means of first-order differential equations. The model was validated from depositions via a design of experiments with central composite planning monitored with the use of sensors to capture the characteristics of the beads (e.g., molten pool, width, penetration, and height).