A Knowledge-Driven Digital Twin Modeling Method for Machining Products Based on Biomimicry

doi:10.3901/JME.2021.23.182

Abstract

Abstract: Real-time observation, analysis, and control of the machining process are the critical parts of optimizing the machining strategy of parts. By fusing multi-dimensional and real-time processing data such as geometry, physical state and equipment state, the modeling and monitoring of the machining process can be realized. The digital twin model is the core and foundation of the digital twin system. However, there is still a lack of a systematic and adaptive development modeling method of high-fidelity, multi-scale, multi-dimensional digital twin model. A knowledge-driven digital twin mimic modeling method for machining products is proposed, which can adaptively construct the digital twin model during the machining process. According to the adaptive evolution of the machining process, the model can express the products in the process in real-time and provide data support for the digital twin decision-making system. Finally, the method is tested in a case on an aerospace part to verify the feasibility of applying the digital twin mimic model in machining.

Key words: digital twin, biomimicry, model evolution, information model

CLC Number:

TG391

LIU Shimin, SUN Xuemin, LU Yuqian, WANG Baicun, BAO Jinsong, GUO Guoqiang. A Knowledge-Driven Digital Twin Modeling Method for Machining Products Based on Biomimicry[J]. Journal of Mechanical Engineering, 2021, 57(23): 182-194.

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