Improving Weld Stability in Gas Metal Arc Welding: A Data-Driven and Machine Learning Approach

The Gas Metal Arc Welding (GMAW) process is widely utilized in industrial production, requiring careful selection of appropriate procedures to ensure the highest quality. A key area of study closely related to GMAW quality is the control of process stability. This research presents a methodology for analyzing welding data to identify instability, thus enabling the development of a stability indicator. Our approach focuses on sensory fusion by integrating multiple sources of information, including sound signals, images, and current signals captured during the welding process. This work explores various configurations of variables to analyze the three primary transfer modes. Additionally, a comprehensive statistical analysis of the results obtained is conducted. Image processing techniques, sound analysis, and artificial intelligence methodologies are employed to enhance the analysis process.