The invention discloses a part digital twin surface model rapid characterization method based on virtual-real fusion, and the method comprises the steps: inputting a part design CAD model and a manufacturing
solid model, and obtaining the surface feature information of the CAD model and the high-precision
point cloud data of the corresponding surface of the
solid model; a mechanism modeling method and a
data analysis method are adopted to obtain a standard
skin shape model and an authenticated
skin shape model respectively; after the two
skin shape models are preprocessed, a training sample set is formed through virtual-real fusion, and a
deep learning model based on a
generative adversarial network is constructed and used for generating a surface three-dimensional deviation form
unit model meeting functional requirements; and establishing correlation mapping between the CAD
ideal surface model of the part and the three-dimensional deviation form of the CAD
ideal surface model to obtain a training
skin surface shape model, and taking the training
skin surface shape model as the representation of the digital twin surface model of the part. According to the method, the precision and efficiency of part surface feature
skin surface model expression are improved, and the method can be suitable for
assembly precision analysis and prediction
simulation of digital twin-driven precision mechanical and electrical products.