CHARACTERIZATION OF A PHASE SEPARATION OF A COATING COMPOSITION

    公开(公告)号:US20220082484A1

    公开(公告)日:2022-03-17

    申请号:US17476785

    申请日:2021-09-16

    IPC分类号: G01N3/08 G01F1/00 G01F23/00

    摘要: A method for detecting a phase separation of a waterborne or solvent-borne or solvent-free coating composition includes providing the coating composition in a receptacle; providing a measurement instrument for receiving the receptacle, the measurement instrument including a measurement probe; controlling the measurement instrument to a) displace the measurement probe through the coating composition along a predefined measurement path with a predefined speed profile, the predefined measurement path extending along a length axis of the receptacle, b) acquire a force-displacement profile by measuring a force exercised on the measurement probe while the probe is being displaced along the predefined measurement path with the predefined speed profile; processing the force-displacement profile for detecting at least one phase separation of the coating composition; and outputting a detection result.

    METHOD FOR GENERATING A COMPOSITION FOR DYES, PAINTS, PRINTING INKS, GRIND RESINS, PIGMENT CONCENTRATES OR OTHER COATING SUBSTANCES

    公开(公告)号:US20220267619A1

    公开(公告)日:2022-08-25

    申请号:US17439962

    申请日:2020-03-13

    IPC分类号: C09D7/80 G06N3/08

    摘要: The method includes using known compositions to train the convolutional neural network, a loss function being minimized for the training; examining whether the value of a loss function meets a predefined criterion, the following steps being carried out selectively in the case where the criterion is not met—selecting a test composition from a set of predefined test compositions by an active learning module; activating a chemical apparatus for producing and examining compositions for paints, varnishes, printing inks, grinding resins, pigment concentrates or other coating substances for the purpose of producing and examining the selected test composition; training the convolutional neural network, using the selected test composition and the properties thereof detected by the apparatus; generating a prediction composition for paints, varnishes, printing inks, grinding resins, pigment concentrates or other coating substances by inputting an input vector into the convolutional neural network; and outputting the prediction composition.

    QUALITATIVE OR QUANTITATIVE CHARACTERIZATION OF A COATING SURFACE

    公开(公告)号:US20220084181A1

    公开(公告)日:2022-03-17

    申请号:US17477025

    申请日:2021-09-16

    IPC分类号: G06T7/00 G01N21/88 G06T7/11

    摘要: A method for qualitative and/or quantitative characterization of a coating surface is provided, comprising: providing a program recognizing coating surface defect types; determining, by the program, whether a camera(s) coupled to the program is within a predefined distance range and/or within a predefined image acquisition angle range relative to a currently presented coating surface; depending on the determination: generating a feedback signal indicative of whether adjustment of the position of the camera(s) is within predefined distance range and/or within the predefined image acquisition angle range, and/or automatically adjusting the relative distance of the camera and and/or automatically adjusting the angle of the camera; enabling the camera to acquire an image of the coating surface only when the camera(s) is/are within the predefined distance range and/or image acquisition angle range; processing the digital image for recognizing coating surface defects; and outputting a characterization of the coating surface.

    QUALITATIVE OR QUANTITATIVE CHARACTERIZATION OF A COATING SURFACE

    公开(公告)号:US20220082508A1

    公开(公告)日:2022-03-17

    申请号:US17476983

    申请日:2021-09-16

    摘要: The invention relates to a method for providing a coating composition-related prediction program, the method comprising: providing a database (204, 904) comprising associations of qualitative and/or quantitative characterizations of coating surfaces and one or more parameters; training a machine learning model for providing a predictive model (M2, M3) having learned to correlate qualitative and/or quantitative characterizations of one or more coating surfaces with one or more of the parameters; and providing a composition-quality-prediction program configured for using the predictive model (M2) for predicting the properties of a coating surface to be produced from one or more input parameters; and/or providing a composition-specification-prediction program configured for using the predictive model (M3) for predicting, based on an input specifying at least a desired coating surface characterization, one or more output parameters related to a coating composition predicted to generate a coating surface having the input surface characterizations.