Abstract

Introduction: To improve product design and production iteratively, virtual-real interaction was made possible by using a Digital Twin (DT) to build an information mapping bridge between the virtual and physical worlds. Digital twins are an important tool in the execution of intelligent manufacturing. Resistance spot welding techniques are used to construct thin sheet structures in many aerospace and automotive industries. The production of high-quality joints during resistance spot welding is highly dependent on the fusion nugget growth process. Due to its large, integrated, complex process, it is difficult to exact quality monitoring using physical models of resistance spot welding.

Method: This study investigates the utilization of digital twin (DT) technology for the purpose of monitoring and enhancing resistance spot welding (RSW) operations, with a specific emphasis on the welding of AA7050/AA5083 materials. A full digital twin model was created by merging realtime data collecting and processing using wavelet threshold noise removal analysis.

Result: The model effectively achieved synchronization with the physical welding process, providing a comprehensive examination of the temperature distribution and the kinetics of nugget production during Resistance Spot Welding (RSW). The digital twin demonstrated remarkable accuracy, achieving a 96% success rate in measuring the diameter of the molten core. This greatly improved the precision of assessing the quality of welding.

Conclusion: These findings validate the digital twin's capacity as a pragmatic instrument for realtime process control and optimization in intricate industrial settings, thus promoting the fusion of virtual models with physical production processes.

Keywords: Intelligent Manufacturing, Digital Twin, Twin Space, Resistance Spot Welding, Wavelet Threshold