Development of digital twin for tungsten inert gas (TIG) welding and analysis using desirability function analysis

This study tackles the challenges of inconsistent weld quality and limited real-time monitoring in Tungsten Inert Gas (TIG) welding by developing a digital twin for the process. The primary objective is to enhance process efficiency and quality control by creating a high-fidelity virtual model of TIG welding that enables real-time monitoring and control. To achieve this, the study employs Desirability Function Analysis (DFA) to optimize multiple quality characteristics. The methodology integrates advanced sensors and IoT technologies to gather real-time data on critical welding parameters such as current, voltage, travel speed, and gas flow rate. This data is used to create a simulation model that accurately replicates the physical welding process. The experiments are conducted using digital twin on SS304 thin which is widely used in industrial applications. By applying DFA, the study converts multiple quality characteristics into a single composite desirability score, offering a quantitative assessment of process efficiency and output quality. The experimental results demonstrate the digital twin’s ability to accurately predict and control welding outcomes, significantly improving productivity and quality control. This research highlights the transformative potential of digital twin technology in TIG welding, paving the way for smarter, data-driven decision-making in industrial settings.