Digital twin modeling method based on biomimicry for machining aerospace components

Liu, Shimin; Bao, Jinsong; Lu, Yuqian; Li, Jie; Lu, Shanyu; Sun, Xiao‐Feng

doi:10.1016/j.jmsy.2020.04.014

Cited by 188 publications

(61 citation statements)

References 37 publications

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“…A digital twin refers to a virtual model that is completely consistent with physical entities in the real world and can simulate an entity's behavior and performance in a real environment in real-time. Digital twin technology has the characteristics of traceability, high integration, real-time, and high precision [33,34]. In the design phase, digital twins can be used for model design, collision detection, pipeline synthesis, and hardcover design.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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“…Digital twins first appeared and played a role in industries of product and manufacturing design, and later emerged in industries such as aerospace (Liu et al 2020), automation (Talkhestani et al 2018), shipbuilding (Mondoro and Grisso 2019), healthcare (Jimenez et al 2020) and energy (Enders and Hoßbach 2019;O'Dwyer et al 2020). In recent years, with the rapid development of technologies and industries such as the Internet of Things(IoT) (Kaur et al 2020), big data (Tao et al 2018), cloud computing (Khatib et al 2019) and artificial intelligence(AI) (Kharchenko et al 2020), the construction basis of smart city has gradually evolved from the original static 3D modeling level towards the digital twin level that combines dynamic digital technology and static 3D model (Yan et al 2019), which forms a new concept that digital twin city assists smart city construction.…”

Section: Digital Twins and Smart Citymentioning

confidence: 99%

Smart city based on digital twins

Shao

2021

Comput.Urban Sci.

133

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Digital twins are considered to be a new starting point for today’s smart city construction. This paper defines the concepts of digital twins and digital twin cities, discusses the relationship between digital twins and smart cities, analyzes the characteristics of smart cities based on digital twins, and focuses on the five main applications of smart cities based on digital twins. Finally, we discuss the future development of smart cities based on digital twins.

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“…Bondarenko et al [83] presented a modeling framework that integrates a continuous time-domain cyclic mean engine model with a crankshaft-angle analytic phenomenal combustion model to fulfill real-time performance constraints. The solution of the digital twin framework would achieve rapidity and precision compared to the conventional method based on differential equations and Runge-Kutta solver [83] . To build a multi-physics digital twin model, Liu et al [84] presented a framework based on biomimicry principles.…”

Section: Engine Performance Optimizationmentioning

confidence: 99%

Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey

Yang¹,

Karimi²,

Kaynak³

et al. 2021

CES

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Digitization and digitalization have already changed our world significantly. Further disruptions are imminent with the ongoing digital transformation, a major component of which is digital twins. As the big data techniques, Internet of Things, cloud computing, and artificial intelligence algorithms advance, the digital twin technology has entered a phase of rapid development. It has been stated to be one of the top ten most promising technologies. Although it is still in its early stages, digital twins are already being widely used in a variety of fields, especially in industry, smart cities, and smart health, which are points that attract most researchers to study. In the literature, there can be seen numerous articles and reviews on digital twins, published every year in these three fields. It is therefore timely, even necessary, to provide an analysis of the published work. This is the motivation behind this article, the focus of which is the major research and application areas of digital twins. The survey first analyzes the recent developments of digital twins, then summarizes the theoretical underpinnings of the technology, and finally concludes with specific developments in various application areas of digital twins. It also discusses the challenges that may be encountered in the future.

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Digital twin modeling method based on biomimicry for machining aerospace components

Cited by 188 publications

References 37 publications

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Smart city based on digital twins

Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey

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