Shape-performance coupled digital twin based on heterogeneous data from multiple sources: a scissor lift platform example

Abstract

Digital twin, a concept of establishing mapping linkages between physical and digital areas using digital technology to achieve instantaneous information transfer for monitoring, optimization or decision-making. Digital twins has emerged as a crucial instrument for ensuring structural safety. However, achieving real-time prediction in time series for structural safety monitoring is challenging, as is the dynamic synthesis of heterogeneous data from numerous sources. This study presents a shape-performance coupled digital twin (SPC-DT) model that integrates heterogeneous data from various sources. The model combines structural analysis, reduced-order processing, and artificial intelligence techniques to incorporate geometric, performance, and sensor data. The aim is to enable dynamic monitoring of structural performance. Furthermore, the deployment of physical space and digital space was accomplished by constructing the SPC-DT model of the scissor lift platform as an illustrative example. The model's effectiveness was validated by a comparison of the measured results, the finite element calculation results, and the SPC-DT model prediction findings. Correlation and error analyses were conducted as part of this verification process. The time required for doing a performance study of complex heavy machinery is greatly decreased by the SPC-DT model. For instance, the SPC-DT prediction saves over 255 times the time cost in the structural prediction of a scissor lift when compared to finite element calculation. This creates a new opportunity for mechanical structure and system safety monitoring.