ENHANCING FACTORY AUTOMATION DEBUGGING WITH DIGITAL TWIN-BASED VIRTUAL DEBUGGING TECHNOLOGY

Abstract

Factory automation lines face challenges such as slow debugging processes and inefficient communication. To address these issues, we propose an efficient virtual debugging technique based on digital twin technology. Our study involves the analysis of a virtual debugging system and the construction of a kinematic model to map motion signals accurately. Additionally, we introduce an enhanced information modelling approach based on the industrial internet information model (3IM) to facilitate seamless information interaction during the debugging process. Furthermore, we employ a dictionary-based compression algorithm and a fast IoT protocol to optimize data transmission, thereby enhancing the overall performance of the virtual debugging system. Our software debugging results demonstrate effective interaction between virtual and physical systems. In the rotation command debugging of a robotic arm, our proposed solution method achieved a shortest iteration time of 0.135s. Interference detection debugging successfully optimized control parameters, and parallel debugging revealed reduced signal reception delay and message delay in motion-driven computing. These findings underscore the excellent application potential of our proposed technique in factory automation line debugging, providing technical support for optimizing virtual debugging systems.