Journal Article

A New General Framework for Response Prediction of Composite Structures Based on Digital Twin with Three Effective Error Correction Strategies

He, Xiaoshu, Ding, Xiang, Wu, Qiong, Zhou, Ling, Qiao, Liang, Peng, Xiuqian, Zuo, Jiale

2023

Overview

In the era of Industry 4.0, researchers in various fields have paid special attention to digital twin technology, which can realize real-time mapping between virtual and physical space. In this paper, a new general framework for response prediction of composite structures based on digital twins is proposed. The tensile testing process of standard samples of carbon fiber-reinforced composites (CFRCs) is used as the twinning object. Moreover, the development of a digital twin and composite structural response prediction based on the generic framework is demonstrated. First, standard CFRC tensile samples are prepared, and relevant raw data are acquired. Subsequently, the microscopic parameters of the standard CFRC tensile samples are obtained by scanning electron microscopy. Geometric measurements are performed to determine the macroscopic parameters, which, together with the material properties of carbon fibers and matrix, are used as the input parameters of a multi-scale virtual physical model (MVPM). The MVPM is used to simulate the actual tensile process using the multi-scale finite element method (FEM). Then, the real-time measurement data from the physical space are transferred to the virtual space through sensors. At the same time, the computationally time-consuming MVPM is downscaled to meet the real-time requirements for the online deployment of the digital twins. In this paper, the backpropagation (BP) neural network model is used to train the input and output parameter data of the MVPM to obtain a reduced-order model (ROM). In addition, to improve the prediction accuracy of the structural response of the digital twin, three model update strategies (MUS) of the ROM are proposed: 1) MUS 1 is based on the ROM, adding the tested sample historical data for the training model update strategy; 2) MUS 2 is based on the ROM 1, adding the measured real-time data of the current sample for training and updating to obtain the ROM 2; 3) MUS 3 is based on the predicted structural response data of ROM 2. Combined with the real-time measured data of the current sample, a higher-order fitting real-time correction is performed to obtain ROM 3. Finally, the tensile process of five CFRC standard samples is demonstrated based on the structural response prediction of the digital twin. The strain response prediction and contour visualization of the whole sample is achieved with limited strain gauge data. By comparison, MUS 2 has higher prediction accuracy than MUS 1 after adding the real-time measured data of the current sample. The prediction errors of MUS 1 and MUS 2 at the later stages of the stretching process are within 10%, with the minimum error of MUS 1 being 15.73% and that of MUS 2 being 3.36%. With the correction of high-order fitting, MUS 3 can achieve a stable prediction error of 20% or less in future moments, and the error can be reduced to less than 5%, reaching a minimum error of 0.44% at the critical tail section near tensile failure.

• Publisher: Springer Nature B.V
• Subject: Accuracy / Back propagation networks / Carbon fibers / Composite structures / Data acquisition / Digital twins / Error correction / Error reduction / Fiber composites / Finite element method / Industry 4.0 / Material properties / Mathematical models / Parameters / Real time / Reduced order models / Strain gauges / Structural response / Tensile tests / Time measurement / Training