公开(公告)号：US12145208B2

公开(公告)日：2024-11-19

申请号：US17471130

申请日：2021-09-09

Applicant: CHONGQING UNIVERSITY

Inventor： Shilong Wang ,  Chi Ma ,  Sibao Wang ,  Dechao Heng ,  Lingwan Zeng ,  Yong Yang ,  Canhui Yang

IPC: B23F11/00 ,  B23F5/22 ,  B23F13/02 ,  B23F23/12 ,  B23Q1/62

Abstract: The present disclosure provides a worm gear machine, including a workbench, a cutter holder and a cutter holder adjusting system, where the cutter holder includes a big bracket, a first slide rail is disposed on the big bracket, a slide seat in sliding fit with the first slide rail is disposed on the first slide rail, a second slide rail is disposed on the slide seat, a small bracket in sliding fit with the second slide rail is disposed on the second slide rail; and a cutter holder spindle is disposed between the big bracket and the slide seat, a cutter bar synchronously rotating with the cutter holder spindle is disposed between an end of the cutter holder spindle facing toward the small bracket and the small bracket, and a gearbox for driving the cutter spindle to rotate is disposed in the big bracket.

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.

公开(公告)号：US12066809B2

公开(公告)日：2024-08-20

申请号：US17470015

申请日：2021-09-09

Applicant: Chongqing University

Inventor： Shilong Wang ,  Chi Ma ,  Sibao Wang ,  Dechao Heng ,  Lingwan Zeng ,  Yong Yang ,  Canhui Yang

IPC: G05B19/40 ,  F16H57/01 ,  G05B19/404 ,  G05B19/406 ,  G06F30/10 ,  G06F30/27 ,  G06N5/01 ,  G06F119/02

CPC classification number: G05B19/404 ,  F16H57/01 ,  G05B19/406 ,  G06F30/10 ,  G06F30/27 ,  G06N5/01 ,  F16H2057/012 ,  G06F2119/02

Abstract: A method for identifying a critical error of a worm gear machine, step 1: obtaining an actual forward kinematic model T27a and an ideal forward kinematic model T27i from a coordinate system of a worm gear hob to a coordinate system of a worm gear, thereby establishing a geometric error-pose error model of the worm gear machine; step 2: regarding the geometric error-pose error model of the worm gear machine as a multi-input multi-output (MIMO) nonlinear system, and solving, by taking the geometric error of each motion axis of the worm gear machine as an input feature X, and a pose error between the worm gear hob and the worm gear as an output variable Y, an importance coefficient of each input feature with a random forest algorithm; and step 3: determining a critical error affecting a machining accuracy of the worm gear machine.