TEMPORARY REMOVAL: Digital twin-driven machining process for multi-process irregular-shaped parts manufacturing

Abstract

With the advancement of high-end manufacturing industries, certain critical parts exhibit peculiar shapes and characteristics of multi-process manufacturing, which intensify milling difficulties and give rise to error propagation effects during the machining process. This necessitates machining systems to be adaptive to different working conditions and capable of real-time error analysis and optimization. Digital twin technology can create virtual replicas of physical entities to observe, analyze, and optimize the machining process. Therefore, this paper proposes a framework for digital twin-driven machining of multi-process irregular-shaped parts, consisting of preparation, processing, and optimization stages. In the preparation stage, a mapping relationship between the workpiece and machine tool motion coordinates is established to create a twin simulation environment consistent with actual operational parameters. In the processing stage, the digital thread dynamically updates the digital twin workpiece and machine tool status in real-time, analyzing the machining errors for each process to assess the finished quality. In the optimization stage, a cloud-based machining knowledge graph is constructed to enhance the precision of machining optimization decisions under different error sources and causes in the digital twin system. Finally, a case study of a typical multi-process irregular-shaped part machining task validates the reliability and superiority of the digital twin-driven method. The case study demonstrates that in a total of 260 machining processes, the digital twin-driven method improves machining accuracy by 40%, saves 34.9% of machining time, and alleviates 70% of machining downtime compared to existing machining methods.