Abstract: A method of inspecting a flexural strength of a glass including a folding area of a display apparatus includes a sample glass surface measurement operation of measuring a three-dimensional shape of a surface of each of a plurality of sample glasses, a sample glass curvature variable calculation operation of calculating a plurality of curvature variables of each of the plurality of sample glasses based on an information measured in the sample glass surface measurement operation, a sample glass flexural strength measurement operation of measuring a flexural strength of each of the plurality of sample glasses, a data analysis operation including a relational expression generation operation of generating a relational expression between the plurality of curvature variables and the flexural strength, and a flexural strength prediction operation of predicting a flexural strength of a target glass based on the relational expression.

Abstract: An embodiment provides an inspection apparatus for a display device, including: a light supplier that supplies light to a surface of the display device; an inspection pattern portion positioned between the display device and the light supplier; a measurement portion that measures reflected light reflected from the surface of the display device; and a processor that processes data of the reflected light measured by the measurement portion, wherein the processor includes a calibration data portion including calibration data and a calibrator calibrating the data using the calibration data of the calibration data portion.

Abstract: A defect classification method includes collecting a first image of an exterior of a display device; determining a defect of the display device based on the first image; extracting XY coordinates of the defect of the display device; collecting a second image of an inside of the display device based on the XY coordinates of the defect of the display device; training a deep machine learning model for determining the defect of the display device and a defect type of the display device based on the second image; determining the defect of the display device based on the second image through the deep machine learning model; and determining the defect type of the display device based on the second image through the deep machine learning model.

Abstract: A method of inspecting a flexural strength of a glass including a folding area of a display apparatus includes a sample glass surface measurement operation of measuring a three-dimensional shape of a surface of each of a plurality of sample glasses, a sample glass curvature variable calculation operation of calculating a plurality of curvature variables of each of the plurality of sample glasses based on an information measured in the sample glass surface measurement operation, a sample glass flexural strength measurement operation of measuring a flexural strength of each of the plurality of sample glasses, a data analysis operation including a relational expression generation operation of generating a relational expression between the plurality of curvature variables and the flexural strength, and a flexural strength prediction operation of predicting a flexural strength of a target glass based on the relational expression.

Abstract: An embodiment provides an inspection apparatus for a display device, including: a light supplier that supplies light to a surface of the display device; an inspection pattern portion positioned between the display device and the light supplier; a measurement portion that measures reflected light reflected from the surface of the display device; and a processor that processes data of the reflected light measured by the measurement portion, wherein the processor includes a calibration data portion including calibration data and a calibrator calibrating the data using the calibration data of the calibration data portion.