<p>Thin-walled parts are prone to deformation during milling because of their large material removal rate, complex structure, and low stiffness. Accurate online prediction and control of machining deformation in thin-walled parts is an urgent problem in the machining process. This paper proposes a theoretical prediction and control framework of machining deformation for thin-walled parts based on digital twin technology. Finite element simulation is used to obtain the machining deformation, and the accuracy of the finite element simulation model is verified by experiments. Based on the measurement data and simulation data, a prediction model of machining deformation of thin-walled parts based on a deep learning algorithm is constructed. The prediction results are visualized in the twin scene. Based on the comparative analysis of the predicted values and the deformation threshold, the effective control of the machining deformation of thin-walled parts is achieved by optimizing the spindle override or feed override, which provides guidance for the manufacturing processes in the physical space. Finally, an example of machining Ti-6Al-4&#xa0;V thin plate is analyzed to verify the correctness and effectiveness of the theoretical framework.</p>