ABSTRACT
Welding is the most significant joining process in various industries for joining similar and dissimilar materials. The critical welding parameters affecting the welded joints and process economics include welding current, welding speed, voltage, flow rate, arc length, and electrode angle. Surface cracks, overlaps, craters, spatter, excessive penetration, arc strike, incomplete penetration, slag inclusion, incomplete fusion, and many other defects are caused by variations in these parameters. Thus, the monitoring, control, and visualization of these parameters are the critical approaches to achieving an efficient and error-free welding process. However, in the present Industry 4.0, among the various monitoring, control, and predictive techniques, Digital Twin is the practical approach for monitoring, fault detection, control, and prediction. Considering the aforementioned, this chapter offers a systematic assessment of the application of digital twin in various welding processes to reduce flaws, real-time fault identification, and parameter optimization to improve quality and process economics.
