Advancing Digital Twin Technology in Manufacturing: A Comprehensive Study on Data Capture and Simulation of End Mills

This research presents a significant advancement in digital twin technology within the manufacturing sector, focusing on enhancing data acquisition and simulation processes during critical product lifecycle phases of endmill: design modeling and finite element analysis (FEA). The core of this study lies in the innovative creation of virtual assets that emulate real-time end milling operations. Our approach leverages CAD/CAE kernels as a pivotal bridge, connecting physical assets with virtual processes through process identification (pid) numbers. This methodology not only captures core utilization and other vital characteristics of computer systems as time series data signals but also introduces a novel angle in the data capture process, enhancing the fidelity of digital twins in manufacturing simulations. A key understanding of our research is the establishment of a direct correlation between CPU signal spikes and enhanced processing capacity, especially in relation to CAD/CAE kernel activities. This insight is critical for informed decision-making in tool and material selection, significantly reducing the development time for new material mathematical models. Our findings underscore the efficiency and practicality of digital twins in real-time milling processes, offering a robust framework for decision-making in both design and simulation phases. The study’s methodological innovations promise substantial contributions to digital twin technology, setting a new benchmark for precision and efficacy in manufacturing simulations.