Designing a Shape–Performance Integrated Digital Twin Based on Multiple Models and Dynamic Data: A Boom Crane Example

Abstract

Abstract The digital twin, a concept that aims to establish a real-time mapping between physical space and virtual space, can be used for real-time analysis, reliability assessment, predictive maintenance, and design optimization of products. This article presents an enabling technology named shape–performance integrated digital twin (SPI-DT) and takes a boom crane as an example to illustrate how to design the SPI-DT step by step for the structural analysis of complex heavy equipment. The SPI-DT contains different types of models, such as an analytical model, a numerical model, and an artificial intelligence (AI) model. In addition, it leverages multisource dynamic data obtained by placing different sensors at multiple measurement positions. In the SPI-DT, the AI model plays a central role, invoking the numerical model and sensor data as the input to predict the structural performance of key components of heavy equipment, while the analytical model analyzes the structure of noncritical components with sensor data as input. This significantly improves the computational efficiency of the digital twin used for the structural analysis of complex heavy equipment, making the digital twin computationally affordable, and thus can be used for the safety assessment and damage protection of the equipment in the operation, as well as the design optimization of next-generation products. Moreover, to visually demonstrate the models and data in the SPI-DT, a three-dimensional application used to display and record the shape and performance information in real time during the operation of the boom crane is developed.