LATTICE STRUCTURE THICKNESSES
    2.
    发明申请

    公开(公告)号:US20250021721A1

    公开(公告)日:2025-01-16

    申请号:US18712648

    申请日:2021-11-23

    Abstract: Examples of methods are described. In some examples, a method may include producing, by a processor, a density determination of a lattice structure. In some examples, the method may include producing, by the processor, a beam thickness determination of the lattice structure. In some examples, the method may include adjusting a beam thickness of the lattice structure based on the density determination and the beam thickness determination.

    GENERATION OF DESIGNS FOR MULTI-FUNCTIONAL OBJECTS

    公开(公告)号:US20220009172A1

    公开(公告)日:2022-01-13

    申请号:US17252318

    申请日:2019-03-29

    Abstract: An example of an apparatus is provided. The apparatus includes an input device to receive design data. The design data includes information about a geometry and load characteristics of an object. The apparatus further includes a structural design engine to generate print data to print the object on a three-dimensional printer based on the design data. The apparatus also includes a fluid design engine to generate fluidic data. The fluidic data represents a fluid channel within the object. In addition, the apparatus includes an output engine to generate an output file based on the print data and the fluidic data

    POINT CLOUD ALIGNMENT
    9.
    发明公开

    公开(公告)号:US20230221698A1

    公开(公告)日:2023-07-13

    申请号:US18011080

    申请日:2020-06-19

    CPC classification number: G05B19/4099 B33Y50/00 G05B2219/49023

    Abstract: Examples of methods for point cloud alignment are described herein. In some examples, a method includes orienting a model point cloud or a scanned point cloud based on a set of initial orientations. In some examples, the method includes determining, using a first portion of a machine learning model, first features of the model point cloud and second features of the scanned point cloud. In some examples, the method includes determining, using a second portion of the machine learning model, correspondence scores between the first features and the second features based on the set of initial orientations. In some examples, the method includes globally aligning the model point cloud and the scanned point cloud based on the correspondence scores.

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