System And Method For Imaging And Evaluating Coating On An Imaging Surface In An Aqueous Inkjet Printer
    81.
    发明申请
    System And Method For Imaging And Evaluating Coating On An Imaging Surface In An Aqueous Inkjet Printer 有权
    用于在水性喷墨打印机中成像和评估成像表面上的涂层的系统和方法

    公开(公告)号:US20140168304A1

    公开(公告)日:2014-06-19

    申请号:US13720333

    申请日:2012-12-19

    CPC classification number: B41J11/0015 B41J2/0057

    Abstract: An inkjet printer is configured to apply a coating material to an imaging surface before an ink image is formed on the surface. At least one optical sensor generates image data of the coating on the imaging surface and identifies a thickness of the coating material. Components of the coating material applicator can be adjusted to keep the thickness of the coating material within a predetermined range.

    Abstract translation: 喷墨打印机被配置为在表面上形成油墨图像之前将涂料施加到成像表面。 至少一个光学传感器产生成像表面上的涂层的图像数据并识别涂层材料的厚度。 可以调节涂料施加器的部件以将涂层材料的厚度保持在预定范围内。

    MACHINE LEARNING FEATURE FEED RATES FOR 3D PRINTING

    公开(公告)号:US20240272612A1

    公开(公告)日:2024-08-15

    申请号:US18168126

    申请日:2023-02-13

    CPC classification number: G05B19/4099 G06N20/00 G05B2219/49023

    Abstract: Systems for and methods of providing a feed rate for three-dimensional printing a part are presented. The disclosed techniques include: obtaining computer readable toolpath instructions for the part, where the toolpath instructions specify a nominal feed rate for a toolpath segment and spatial toolpath data of the toolpath segment; providing an input including the spatial toolpath data to a trained machine learning system, where the trained machine learning system has been trained using training data including: training spatial toolpath data, training closed loop gain data, and training feed rate data; obtaining a revised feed rate for the toolpath segment different from the nominal feed rate for the toolpath segment, where the revised feed rate is output from the trained machine learning system; and providing revised computer readable toolpath instructions, where the revised machine learning toolpath instructions include the revised feed rate.

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