Digital twin-based tig welding quality prediction using electrode tip angle degradation influencing Industry 5.0 in manufacturing sector

Abstract

Automation in tungsten inert gas (TIG) welding is important to achieve high production rates and quality in manufacturing industries. To improve the welding process and quality inspection methodologies, the intelligent welding robot and vision-based inspection system have been researched and deployed in many engineering fields. Hence to enhance the performance and production, a digital twin-based welding system with the prediction of weld quality based on the consideration of electrode tip angle degradation. The proposed system will capture real-time electrode tip angle and weld pool temperature using a forward looking infrared (FLIR) camera along with welding current and speed correlated with tensile strength as the output parameter. To validate the analysis, support vector machine (SVM) and random forest (RF) algorithms were implemented in which the RF model performs well on the prediction of welding quality by mapping with tensile strength. RF model confirms maximum accuracy of 90% with 0.29 seconds computation time to perform prediction on the next execution of welding operation. It is inferred that if the tip angle degradation increases consecutively welding current decreases drastically impacting the weld quality from good to poor. To forecast the need for immediate or scheduled maintenance to reduce the tip angle degradation, a linear regression algorithm is implemented to enable the inspection engineer to perform maintenance without delay in production.