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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
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.
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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.
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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.
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