Research on online deformation monitoring of thin-walled parts driven by digital twin

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Thin-walled parts are widely used in aerospace. Because thin-walled parts have thin walls, weak stiffness, and are easy to deform and other characteristics, it is easy to produce deformation in the processing, affecting the quality and accuracy of the workpiece processing. Therefore, accurate and efficient online real-time monitoring of the deformation of thin-walled parts has become a vital issue in process control. The visualization, real-time monitoring, and iterative optimization of digital twin technology can effectively solve the problem of online monitoring of thin-walled parts processing. Therefore, this paper proposes a digital twin driven deformation monitoring method for thin-walled parts, which realizes the effective monitoring of deformation during the processing of thin-walled parts. Firstly, the digital twin architecture for online deformation monitoring of thin-walled parts is established, and the critical technologies of the digital twin for online deformation monitoring of thin-walled parts are proposed. Secondly, the Support Vector Regression (SVR) finite element surrogate model is established to realize the rapid simulation of thin-walled parts cutting. At the same time, a One-Dimensional Convolutional Neural Network (1DCNN) combined with the Self-Attention Mechanism (SAM) deformation prediction model was established based on sensor data. The real-time monitoring and accurate prediction of the deformation of thin-walled components are realized through the parallel guidance of the deep learning and surrogate model. Finally, an experimental analysis was carried out by machining thin-walled plates of aluminum alloy 7075, and the fit of the monitoring models was above 95%, thus verifying the validity of the proposed method.