ALIGNER IMAGE BASED QUALITY CONTROL SYSTEM
    4.
    发明申请

    公开(公告)号:US20200242765A1

    公开(公告)日:2020-07-30

    申请号:US16851038

    申请日:2020-04-16

    Abstract: A system for inspecting a customized dental device associated with a dental application for manufacturing defects is disclosed. The system obtains images of the customized device and identifies an identifier of the customized device. The system determines a digital file associated with the customized device based on the identifier, the digital file including a first digital model of the customized device and/or a second digital model of a mold used during manufacture of the customized device. The system determines an intended property for the customized device based on at least one of the first digital model or the second digital model, determines an actual property of the customized device from the images, determines whether there is a manufacturing defect in the customized device by comparing the intended property for the customized device with the actual property of the customized device, and outputs an output associated with the determination.

    Machine based three-dimensional (3D) object defect detection

    公开(公告)号:US11189021B2

    公开(公告)日:2021-11-30

    申请号:US16685848

    申请日:2019-11-15

    Abstract: Implementations describe systems and methods for machine based defect detection of three-dimensional (3D) printed objects. A method of one embodiment of the disclosure includes providing a first illumination of a 3D printed object using a first light source arrangement. A plurality of images of the 3D printed object are then generated using one or more imaging devices. Each image may depict a distinct region of the 3D printed object. The plurality of images may then be processed by a processing device using a machine learning model trained to identify one or more types of manufacturing defects of a 3D printing process. The machine learning model may provide a probability that an image contains a manufacturing defect. The processing device may then determine, without user input, whether the 3D printed object contains one or more manufacturing defects based on the results provided by the machine learning model.

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