Method for constructing body-in-white spot welding deformation prediction model based on graph convolutional network
US12093019B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 2, 2022 |
| Grant date | Sep 17, 2024 |
| Priority date | — |
| Expiry date | Mar 31, 2043 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02T10/40
- WIPO fieldControl
- WIPO sectorInstruments
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.