Liquid crystal glass quality detection system based on buckling deformation

Abstract

The invention discloses a liquid crystal glass quality detection system based on buckling deformation, and relates to the technical field of glass substrate processing, a data acquisition module is used for acquiring glass substrate size related data, then a data processing module is used for preprocessing and marking, and the buckling bending degree is calculated to obtain a buckling bending coefficient; then acquiring a warping bending degree set stored in the control center through a deformation analysis module, performing parameter matching on the warping bending coefficient and the warping bending degree set, marking the corresponding glass substrate, counting the number of standard glass substrates, and determining whether the batch of glass substrates are qualified or not according to a set number standard; and a corresponding signal is sent to the execution module according to the detection result of whether the glass substrates are qualified or not to give an alarm prompt, so that the functions of calculating the warping degree of a batch of glass substrates and judging whether the glass substrates are qualified or not are realized.

Description

Technical Field

[0001] The invention belongs to the technical field of glass substrate processing, and relates to a liquid crystal glass quality detection system, in particular to a liquid crystal glass quality detection system based on warping deformation. Background Art

[0002] As a new domestic industry, glass substrate production has just started, and inspection items and inspection methods are constantly being established and improved. The inspection of glass substrate warping is an important inspection item for monitoring product warping quality and is essential. Poor product warping quality can cause multiple defects on the user side, resulting in great economic losses. Therefore, it is very necessary and important to conduct warping and deformation inspection and size verification inspection on glass substrates during the production process of liquid crystal glass substrates. Summary of the invention

[0003] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a liquid crystal glass quality detection system based on warping deformation, which can calculate the warping degree of a batch of substrate glasses and determine whether the quality of this batch of substrate glasses is qualified.

[0004] In a first aspect, the purpose of the present invention can be achieved by the following technical solution: a liquid crystal glass quality detection system based on warping deformation, comprising:

[0005] Data acquisition module: used to collect data related to the size of the glass substrate, and send the data related to the size of the glass substrate to the data processing module, wherein the data related to the size of the glass substrate includes: the thickness of the glass substrate, the height of the highest point of the glass substrate, and the height of the lowest point of the glass substrate;

[0006] Data processing module: used for preprocessing the glass substrate size-related data, marking the preprocessed glass substrate size-related data, calculating the warping curvature using the marked glass substrate size-related data, obtaining the warping curvature coefficient, and sending the warping curvature coefficient to the deformation analysis module;

[0007] Deformation analysis module: used to obtain the warping curvature set stored in the control center, perform parameter matching between the received warping curvature coefficient and the warping curvature set, determine the warping curvature degree of the glass substrate according to the matching result, and mark the corresponding glass substrate. The marking categories include: glass substrates with too little warp, glass substrates with too much warp, and standard glass substrates. The number of standard glass substrates, glass substrates with too little warp, and glass substrates with too much warp are counted, and the number standard is set to determine whether this batch of glass substrates is qualified. If qualified, a qualified detection result signal is sent to the execution module. If unqualified, an unqualified detection result signal is sent to the execution module.

[0008] Control center: used to obtain and store the warping curvature set;

[0009] Execution module: used to, after receiving a qualified test result signal, prompt through an alarm that the batch of glass substrates is qualified, and after receiving a unqualified test result signal, prompt through an alarm that the batch of glass substrates is unqualified.

[0010] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: the data identification process of the data processing module includes: marking the thickness of the glass substrate as Ti, marking the height of the highest point of the glass substrate as Lgi; marking the height of the lowest point of the glass substrate as Ldi, wherein i is the number of times the glass substrate size-related data is collected by the data acquisition module, and i=1, 2, 3, ..., n, and n is the total number of times the glass substrate size-related data is collected by the data acquisition module.

[0011] In combination with the first aspect, in some implementations of the first aspect, the system further includes: a calculation formula for calculating the warping curvature of the data processing module is as follows:

[0012]

[0013] Where Qqi is the warping coefficient, k 1 k is the influence coefficient of the highest point height of the glass substrate, 2 k is the influence coefficient of the lowest point height of the glass substrate, 3 is the influence coefficient of glass substrate thickness, K is the measurement correlation coefficient, and exp() represents an exponential function with the natural constant e as the base.

[0014] In combination with the first aspect, in some implementations of the first aspect, the system further includes: an analysis process of the deformation analysis module:

[0015] Obtain a warping curvature set {Qq0} stored in the control center, wherein the minimum value in the warping curvature set is Qqmin, and the maximum value in the warping curvature set is Qqmax;

[0016] If Qqi<Qqmin, the glass substrate corresponding to the corresponding warping bending coefficient is marked as a glass substrate with too small warping;

[0017] If Qqi>Qqmax, the glass substrate corresponding to the corresponding warping bending coefficient is marked as a glass substrate with excessive warping;

[0018] If Qqmin≤Qqi≤Qqmin, the glass substrate corresponding to the corresponding warping coefficient is marked as a standard glass substrate.

[0019] In combination with the first aspect, in some implementations of the first aspect, the system further includes: a determination process of the deformation analysis module:

[0020] The number of standard glass substrates, the number of glass substrates with too little warping, and the number of glass substrates with too much warping are counted and marked as R1, R2, and R3 respectively. If R1≥2(R2+R3), the batch of glass substrates is determined to be qualified glass substrates, and the deformation analysis module sends a qualified detection result signal to the execution module;

[0021] If R1<2(R2+R3), the batch of glass substrates is determined to be unqualified glass substrates, and the deformation analysis module sends an unqualified detection result signal to the execution module; wherein, R1+R2+R3=n.

[0022] In combination with the first aspect, in some implementations of the first aspect, the system further includes: the process of acquiring the warpage curvature set in the control center includes the following steps:

[0023] Using a server in a control center to collect glass substrate size data, wherein the glass substrate size data includes glass substrate thickness data, glass substrate highest point height data, and glass substrate lowest point height data;

[0024] The standard warping determination model is combined with the glass substrate size data in the video to obtain the warping determination coefficient, and all the obtained warping determination coefficients are integrated to generate a warping set, wherein the standard warping determination model is trained based on an artificial intelligence model.

[0025] Beneficial effects of the present invention:

[0026] The present invention collects glass substrate size-related data through a data acquisition module, and then uses a data processing module to perform preprocessing and identification, and then calculates the warping curvature to obtain a warping curvature coefficient, and then obtains a warping curvature set stored in a control center through a deformation analysis module, performs parameter matching between the warping curvature coefficient and the warping curvature set, marks the corresponding glass substrates, and counts the number of standard glass substrates, determines whether this batch of glass substrates is qualified according to a set quantity standard, and sends a corresponding signal to an execution module for an alarm prompt according to the qualified detection result, thereby realizing the function of being able to calculate the warping of a batch of glass substrates and determine whether the quality of this batch of glass substrates is qualified. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Embodiment 1:

[0031] The following is an introduction to the relevant terms involved in the embodiments of the present application:

[0032] Warp is a spatial measurement method used to describe the degree of curvature of a plane in space.

[0033] Warpage (or warp) is used to describe the degree of curvature of a plane in space. It is numerically defined as the distance between the two points on the warped plane that are farthest apart in the height direction. The warp of an absolute plane is 0.

[0034] Warpage is one of the important indicators for evaluating product quality. In the existing evaluation system, the evaluation of warpage is carried out by warpage, generally by the maximum warpage or the warpage of a specific part. When using injection molding CAE for design quality prediction, the evaluation of the warpage simulation results is generally carried out directly by the maximum warpage, and sometimes the total average warpage, the average warpage of the 10% maximum warpage node and other statistics are used as indicators for evaluating warpage.

[0035] For the same product with the same warpage influence mode, these indicators can describe the magnitude of warpage deformation in different designs. However, for factors such as different materials and mold structures that cause changes in the influence mode of warpage deformation, the severity of warpage deformation of each part under different designs also changes accordingly. At this time, using the maximum warpage deformation or other relative statistics to evaluate the warpage deformation has great limitations and cannot better reflect the user satisfaction of the product warpage deformation under different designs. Especially for liquid crystal glass, using the maximum warpage deformation or other statistics to characterize the warpage deformation of the product has great limitations.

[0036] like Figure 1 As shown, a liquid crystal glass quality detection system based on warping deformation includes:

[0037] Data acquisition module, data processing module, deformation analysis module, control center and execution module;

[0038] The data acquisition module is used to collect data related to the size of the glass substrate, and send the collected data related to the size of the glass substrate to the data processing module for processing. Specifically, the data related to the size of the glass substrate includes: the thickness of the glass substrate, the height of the highest point of the glass substrate, and the height of the lowest point of the glass substrate;

[0039] It should be noted that the height of the highest point of the glass substrate is the height of the highest point of the glass substrate from the horizontal plane, and the height of the lowest point of the glass substrate is the height of the lowest point of the glass substrate from the horizontal plane; the thickness of the glass substrate is measured by a vernier caliper, and the thickness of the glass substrate can be measured more accurately by clamping both ends of the glass substrate on both sides of the vernier caliper, and the measurement of the height of the highest point of the glass substrate and the height of the lowest point of the glass substrate is obtained by measuring with a tape measure;

[0040] After receiving the glass substrate size related data sent by the data acquisition module, the data processing module performs data processing. Specifically, the processing process of the data processing module includes the following steps:

[0041] Preprocessing the data related to the size of the glass substrate, wherein the preprocessing mainly includes:

[0042] Data cleaning, data integration, data transformation, data reduction, etc. These data processing technologies are used before data mining, which greatly improves the quality of data mining patterns and reduces the time required for actual mining.

[0043] In this embodiment, the data cleaning is mainly performed on the glass substrate size related data. The data cleaning routine "cleans" the data by filling in missing values, smoothing noisy data, identifying or deleting outliers, and resolving inconsistencies. The main goals are to achieve the following: format standardization, abnormal data removal, error correction, and duplicate data removal.

[0044] The pre-processed glass substrate size-related data is labeled, wherein the glass substrate thickness is labeled as Ti, the highest point height of the glass substrate is labeled as Lgi, and the lowest point height of the glass substrate is labeled as Ldi, wherein i is the number of times the glass substrate size-related data is collected by the data collection module, and i=1, 2, 3, ..., n, and n is the total number of times the glass substrate size-related data is collected by the data collection module; it should be noted that n is the total number of glass substrates to be tested in this batch;

[0045] The warping curvature is calculated using the marked glass substrate size-related data to obtain the warping curvature coefficient, wherein the calculation formula for the warping curvature calculation of the data processing module is as follows:

[0046] Using the formula

[0047] The warping bending coefficient Qqi is calculated;

[0048] In the formula, k 1 k is the influence coefficient of the highest point height of the glass substrate, 2 k is the influence coefficient of the lowest point height of the glass substrate, 3 is the influence coefficient of glass substrate thickness, K is the measurement correlation coefficient, and exp() represents an exponential function with the natural constant e as the base;

[0049] Among them, in this embodiment, the influence coefficient of the height of the highest point of the glass substrate, the influence coefficient of the height of the lowest point of the glass substrate and the influence coefficient of the thickness of the glass substrate are calculated by comprehensive evaluation based on the influence of external factors when the thickness of the glass substrate, the height of the highest point of the glass substrate and the height of the lowest point of the glass substrate are obtained daily in this application, including human factors, machine detection and environmental factors, etc.; human factors are some factors caused by human operation or improper scanning; the measurement correlation coefficient is obtained through relevant data caused by errors including glass substrate material and measurement accuracy in the daily measurement process.

[0050] Send the calculated warping bending coefficient Qqi to the deformation analysis module for analysis;

[0051] Obtain a warping curvature set {Qq0} stored in the control center, wherein the minimum value in the warping curvature set is Qqmin, and the maximum value in the warping curvature set is Qqmax;

[0052] Perform parameter matching on the received warping coefficient Qqi and the warping degree set {Qq0}, and determine the warping degree of the glass substrate according to the matching result;

[0053] If Qqi<Qqmin, it is determined that the degree of warping is less than the minimum value in the stored warping degree set, and the glass substrate corresponding to the corresponding warping coefficient is marked as a glass substrate with too small warping;

[0054] If Qqi>Qqmax, it is determined that the degree of warping is greater than the maximum value in the stored warping degree set, and the glass substrate corresponding to the corresponding warping coefficient is marked as a glass substrate with excessive warping;

[0055] If Qqmin≤Qqi≤Qqmin, it is determined that the warping degree is within the warping degree set, and the glass substrate corresponding to the warping coefficient is marked as a standard glass substrate;

[0056] The number of standard glass substrates, the number of glass substrates with too little warping, and the number of glass substrates with too much warping are counted and marked as R1, R2, and R3 respectively. If R1≥2(R2+R3), the batch of glass substrates is determined to be qualified glass substrates, and the deformation analysis module sends a qualified detection result signal to the execution module;

[0057] If R1<2(R2+R3), the batch of glass substrates is determined to be unqualified glass substrates, and the deformation analysis module sends an unqualified detection result signal to the execution module; wherein R1+R2+R3=n;

[0058] The control center is used to obtain and store the warping curvature set {Qq0}, wherein the process of obtaining the warping curvature set includes the following steps:

[0059] Using a server in a control center to collect glass substrate size data, wherein the glass substrate size data includes glass substrate thickness data, glass substrate highest point height data, and glass substrate lowest point height data;

[0060] The standard warping determination model is combined with the glass substrate size data in the video to obtain the warping determination coefficient, and all the obtained warping determination coefficients are integrated to generate a warping set, wherein the standard warping determination model is trained based on an artificial intelligence model.

[0061] The process of training the standard warpage determination model based on the artificial intelligence model:

[0062] Acquire a standard glass substrate size coefficient through a server, wherein the standard glass substrate size coefficient includes a standard glass substrate thickness coefficient, a standard glass substrate highest point height coefficient, and a standard glass substrate lowest point height coefficient;

[0063] The artificial intelligence model is trained by using the standard glass substrate size coefficient to obtain and store a standard warping and bending determination model; wherein the artificial intelligence model includes a deep convolutional neural network model and an RBF neural network model.

[0064] After receiving the qualified detection result signal sent by the deformation analysis module, the execution module prompts that the batch of glass substrates is qualified through an alarm, and after receiving the unqualified detection result signal sent by the deformation analysis module, the execution module prompts that the batch of glass substrates is unqualified through an alarm.

[0065] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0066] It needs to be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0067] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0068] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0069] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A liquid crystal glass quality detection system based on warping deformation, characterized in that: include: Data acquisition module: used to collect data related to the size of the glass substrate, and send the data related to the size of the glass substrate to the data processing module, wherein the data related to the size of the glass substrate includes: the thickness of the glass substrate, the height of the highest point of the glass substrate, and the height of the lowest point of the glass substrate; Data processing module: used for preprocessing the glass substrate size-related data, marking the preprocessed glass substrate size-related data, calculating the warping curvature using the marked glass substrate size-related data, obtaining the warping curvature coefficient, and sending the warping curvature coefficient to the deformation analysis module; Deformation analysis module: used to obtain the warping curvature set stored in the control center, perform parameter matching between the received warping curvature coefficient and the warping curvature set, determine the warping curvature degree of the glass substrate according to the matching result, and mark the corresponding glass substrate. The marking categories include: glass substrates with too little warp, glass substrates with too much warp, and standard glass substrates. The number of standard glass substrates, glass substrates with too little warp, and glass substrates with too much warp are counted, and the number standard is set to determine whether this batch of glass substrates is qualified. If qualified, a qualified detection result signal is sent to the execution module. If unqualified, an unqualified detection result signal is sent to the execution module. Control center: used to obtain and store the warping curvature set; Execution module: used to, after receiving a qualified test result signal, prompt through an alarm that the batch of glass substrates is qualified, and after receiving a unqualified test result signal, prompt through an alarm that the batch of glass substrates is unqualified.

2. The liquid crystal glass quality detection system based on warping deformation according to claim 1, characterized in that: The data identification process of the data processing module includes: marking the thickness of the glass substrate as Ti, marking the height of the highest point of the glass substrate as Lgi; marking the height of the lowest point of the glass substrate as Ldi, wherein i is the number of times the glass substrate size-related data is collected by the data collection module, and i=1, 2, 3, ..., n, and n is the total number of times the glass substrate size-related data is collected by the data collection module.

3. The liquid crystal glass quality detection system based on warping deformation according to claim 2, characterized in that: The calculation formula for the warping curvature of the data processing module is as follows: Wherein, Qqi is the warpage bending coefficient, k1 is the influence coefficient of the highest point height of the glass substrate, k2 is the influence coefficient of the lowest point height of the glass substrate, k3 is the influence coefficient of the thickness of the glass substrate, K is the measurement correlation coefficient, and exp() represents an exponential function with the natural constant e as the base.

4. The liquid crystal glass quality detection system based on warping deformation according to claim 1, characterized in that: The analysis process of the deformation analysis module: Obtain a warping curvature set {Qq0} stored in the control center, wherein the minimum value in the warping curvature set is Qqmin, and the maximum value in the warping curvature set is Qqmax; If Qqi<Qqmin, the glass substrate corresponding to the corresponding warping bending coefficient is marked as a glass substrate with too small warping; If Qqi>Qqmax, the glass substrate corresponding to the corresponding warping bending coefficient is marked as a glass substrate with excessive warping; If Qqmin≤Qqi≤Qqmin, the glass substrate corresponding to the corresponding warping coefficient is marked as a standard glass substrate.

5. The liquid crystal glass quality detection system based on warping deformation according to claim 4, characterized in that: The determination process of the deformation analysis module: The number of standard glass substrates, the number of glass substrates with too little warping, and the number of glass substrates with too much warping are counted and marked as R1, R2, and R3 respectively. If R1≥2(R2+R3), the batch of glass substrates is determined to be qualified glass substrates, and the deformation analysis module sends a qualified detection result signal to the execution module; If R1<2(R2+R3), the batch of glass substrates is determined to be unqualified glass substrates, and the deformation analysis module sends an unqualified detection result signal to the execution module; wherein, R1+R2+R3=n.

6. The liquid crystal glass quality detection system based on warping deformation according to claim 1, characterized in that: The process of obtaining the warpage curvature set in the control center includes the following steps: Using a server in a control center to collect glass substrate size data, wherein the glass substrate size data includes glass substrate thickness data, glass substrate highest point height data, and glass substrate lowest point height data; The standard warping determination model is combined with the glass substrate size data in the video to obtain the warping determination coefficient, and all the obtained warping determination coefficients are integrated to generate a warping set, wherein the standard warping determination model is trained based on an artificial intelligence model.

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