公开(公告)号：US12093019B2

公开(公告)日：2024-09-17

申请号：US17830361

申请日：2022-06-02

Applicant: Chongqing University

Inventor： Shilong Wang ,  Bo Yang ,  Lili Yi ,  Ling Kang ,  Yu Wang

IPC: A41H3/00 ,  B23K31/00 ,  G05B19/4097 ,  G05B19/4099

CPC classification number: G05B19/4099 ,  B23K31/003 ,  G05B2219/49007

Abstract: A method for constructing a body-in-white (BiW) spot welding deformation prediction model based on a graph convolutional network (GCN) includes: 1) acquiring a welding feature and 3D coordinates of a spot weld to form an eigenvector and extracting designed 3D coordinates at each 3D coordinate measurement point; 2) encoding, by an encoder, eigenvectors and designed 3D coordinate vectors into hidden space vectors of spot welds and hidden space vectors of the coordinate measurement points, respectively, and constructing a graph topology G through a k-nearest neighbors algorithm; 3) decomposing a Laplacian eigenvector of the constructed graph topology G to acquire frequency domain components, and linearly transforming eigenvalues corresponding to the frequency domain components to construct a multi-layer GCN; 4) inputting the thermodynamic and kinetic information of each coordinate measurement point into a deep neural network and decoding a final deformation at each coordinate measurement point; and 5) optimizing the model.