Kettle cover mold deformation detection method, system and equipment

Abstract

The invention relates to the technical field of mold deformation detection, in particular to a kettle cover mold deformation detection method, system and equipment, and the method comprises the steps: determining a symmetric matrix of each matrix based on the position distribution of each matrix in a corresponding contour matrix; based on the distribution condition of all quantized contour data in each square matrix and the symmetrical square matrix thereof, and the similarity of all column vectors between each square matrix and the opposite square matrix thereof, and in combination with the complexity of each square matrix and the symmetrical square matrix thereof, determining the distinction degree between each square matrix and the symmetrical square matrix thereof; and determining an abnormal value of each kettle cover mold based on the difference between different measurements of the distinction degrees between all the square matrixes and the respective symmetrical square matrixes, and detecting the kettle cover molds. The outline complexity and the deformation probability of the kettle cover mold are comprehensively analyzed, and the sensitivity of deformation detection of the kettle cover mold is improved.

Description

Technical Field

[0001] The present application relates to the technical field of mold deformation detection, and in particular to a method, system and equipment for detecting deformation of a kettle cover mold. Background Art

[0002] Injection molding is one of the main ways to produce kettle lids. In order to ensure the sealing between the kettle lid and the pot mouth, there are high requirements for the injection molding accuracy of the kettle lid injection molding. During the kettle lid injection molding process, the extrusion and collision of the kettle lid mold parting surface during the mold opening and closing process, the pressure of the melt in the kettle lid mold cavity on the inner wall of the cavity, and the clamping effect of the injection molding machine clamping force cause the kettle lid mold to undergo elastic deformation in a short period of time. After long-term operation, the cumulative effect of various factors will cause permanent plastic deformation of some parts of the kettle lid mold, resulting in a decrease in the dimensional accuracy of the injection-molded kettle lid, affecting the sealing between the kettle lid and the pot mouth. Therefore, it is necessary to regularly detect the deformation of the kettle lid mold and replace it in time when the mold is deformed.

[0003] Laser profile scanning is a non-contact measurement method that improves measurement accuracy while avoiding the mold clamping problem in the traditional measurement process. It is now a commonly used method for detecting deformation of kettle lid molds. At present, when using a laser profile scanner to detect kettle lid molds, it is only judged whether it has deformed based on the difference in mold profile data collected twice adjacently, ignoring the different deformation probabilities in different areas of the mold. In areas with low deformation probability, even a small measurement difference may lead to misjudgment as deformation, thereby causing unnecessary maintenance or inspection. In areas with high deformation rates, if the deformation amplitude is smaller than the detection resolution, or the deformation mode does not match the detection method, the real deformation problem may be missed. The above two situations will lead to inaccurate assessment of the health of the mold, thereby reducing the sensitivity of the kettle lid mold deformation detection. Summary of the invention

[0004] In a first aspect, an embodiment of the present application provides a method for detecting deformation of a kettle cover mold, the method comprising the following steps: Within a preset time, each kettle lid mold is scanned multiple times by using a laser profile scanner, the profile data of all scanning points in each scan are measured, and the profile data vectors of each scan are formed, the profile data vectors of all scans form a profile matrix of each kettle lid mold, and the profile matrix is ​​evenly divided into multiple square matrices, wherein the profile data is the distance from the laser beam emitted by the laser profile scanner to all scanning points; In each kettle lid mold, based on the maximum value of all contour data in each square matrix, the contour change interval of each square matrix is ​​determined, and the contour change interval is evenly divided into multiple sub-intervals of equal length. Based on the proportion of all contour data in each sub-interval in each square matrix in all contour data in the square matrix, and the degree of discreteness of the number of rows and columns of all contour data in each sub-interval in each square matrix, the complexity of each square matrix is ​​determined; Based on the position distribution of each square matrix in its corresponding contour matrix, determine the square matrix that is positionally symmetrical with each square matrix, and record the square matrix that is positionally symmetrical with each square matrix as the symmetric square matrix of each square matrix; replace all contour data in each sub-interval of each square matrix with the value of the upper limit of the sub-interval, record all the replaced data as quantized contour data, and based on the distribution of all quantized contour data in each square matrix and its symmetric square matrix, and the similarity of all column vectors between each square matrix and its opposite square matrix, determine the symmetry coefficient between each square matrix and its symmetric square matrix, and combine the complexity of each square matrix and its symmetric square matrix to determine the discrimination between each square matrix and its symmetric square matrix; Based on the difference in the degree of distinction between all square arrays and their symmetrical square arrays in different measurements, the outlier value of each kettle lid mold is determined and the kettle lid mold is tested.

[0005] Preferably, the method for determining the contour change interval of each square matrix is: The maximum and minimum values ​​of all contour data in each square matrix are used as the upper and lower limits of the contour change range respectively.

[0006] Preferably, the expression of the complexity of each square matrix is: ; In the formula, represents the complexity of square matrix i; , They represent the discrete degree of the number of rows and the discrete degree of the number of columns of all contour data in subinterval k in square matrix i respectively; It represents the proportion of all contour data in subinterval k in square matrix i to all contour data in square matrix i; represents the number of all subintervals in the contour change interval of square matrix i; ln( ) represents the logarithmic function with a natural constant as the base.

[0007] Preferably, determining a square matrix that is positionally symmetrical to each square matrix comprises: In the contour matrix of each kettle cover mould, the contour data are symmetrically distributed about the central axis of the kettle cover mould according to the shape of the kettle cover mould. If square matrix D and square matrix E are mirror-symmetrical about the central axis of the kettle cover mould, then square matrix D and square matrix E are positionally symmetrical matrices.

[0008] Preferably, the method for determining the symmetry coefficient between each square matrix and its symmetric square matrix is: All quantized contour data of each square matrix are used as the input of the maximum inter-class variance algorithm, and the maximum inter-class variance of each square matrix is ​​output; Symmetry coefficient between matrix i and its symmetric matrix The expression is: ; In the formula, It represents the sum of the maximum between-class variance of matrix i and the maximum between-class variance of the symmetric matrix of matrix i; Represents the jth column vector of square matrix i and the jth column vector of the symmetric square matrix of square matrix i The similarity between column vectors; W represents the total number of columns in each square matrix; norm() represents the normalization function.

[0009] Preferably, the method for determining the degree of distinction between each square matrix and its symmetric square matrix is: The sum of the complexity of each square matrix and its symmetric square matrix is ​​calculated, and the ratio of the sum to the symmetry coefficient is used as the discrimination between each square matrix and its symmetric square matrix.

[0010] Preferably, the outlier value of each kettle cover mold is a normalized result of taking the average of the differences between all measurements of the discrimination between all square matrices in each kettle cover mold and its symmetric square matrices.

[0011] Preferably, the process of detecting the kettle cover mold is: The mean of the outliers of all kettle cover molds is taken as the deformation threshold. If the outlier value of the kettle cover mold is greater than the deformation threshold, the kettle cover mold is deformed; otherwise, the kettle cover mold is not deformed.

[0012] In a second aspect, an embodiment of the present application provides a kettle cover mold deformation detection system, the system comprising: The kettle cover mold data acquisition module is used to use a laser profile scanner to scan each kettle cover mold multiple times within a preset time, measure the profile data of all scanning points in each scan, and form a profile data vector for each scan, and the profile data vectors of all scans constitute a profile matrix of each kettle cover mold, and evenly divide the profile matrix into multiple square matrices, wherein the profile data is the distance from the laser beam emitted by the laser profile scanner to all scanning points; A kettle cover mold weight acquisition module is used to determine the contour change interval of each square matrix in each kettle cover mold based on the maximum value of all contour data in each square matrix, evenly divide the contour change interval into multiple sub-intervals of equal length, and determine the complexity of each square matrix based on the proportion of all contour data in each sub-interval in each square matrix in all contour data in the square matrix, and the discrete degree of the number of rows and columns of all contour data in each sub-interval in each square matrix; Based on the position distribution of each square matrix in its corresponding contour matrix, determine the square matrix that is positionally symmetrical with each square matrix, and record the square matrix that is positionally symmetrical with each square matrix as the symmetric square matrix of each square matrix; replace all contour data in each sub-interval of each square matrix with the value of the upper limit of the sub-interval, record all the replaced data as quantized contour data, and based on the distribution of all quantized contour data in each square matrix and its symmetric square matrix, and the similarity of all column vectors between each square matrix and its opposite square matrix, determine the symmetry coefficient between each square matrix and its symmetric square matrix, and combine the complexity of each square matrix and its symmetric square matrix to determine the discrimination between each square matrix and its symmetric square matrix; The kettle cover mold detection module is used to determine the abnormal value of each kettle cover mold based on the difference between the discrimination between all square arrays and their symmetrical square arrays in different measurements, and detect the kettle cover mold.

[0013] In a third aspect, an embodiment of the present application further provides a kettle cover mold deformation detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned kettle cover mold deformation detection methods when executing the computer program.

[0014] It can be seen from the above embodiments that the kettle cover mold deformation detection method provided in the embodiments of the present application has at least the following beneficial effects: The present application uses a laser profile scanner to scan each kettle lid mold multiple times within a preset time, measures the profile data of all scanning points in each scan, and forms a profile data vector for each scan, and the profile data vectors of all scans constitute a profile matrix of each kettle lid mold, and the profile matrix is ​​evenly divided into a plurality of square arrays; in each kettle lid mold, based on the extreme distribution of all profile data in each square array, the profile change interval of each square array is determined, the profile change interval is evenly divided into a plurality of sub-intervals, and the complexity of each square array is determined based on the proportion of all profile data in each sub-interval in all profile data in the square array, and the degree of discreteness of the number of rows and columns of all profile data in each sub-interval in each square array. The beneficial effect is that the correlation characteristics between the profile complexity of the kettle lid mold and the probability of its deformation are utilized, and the distribution of the size and position of the profile data in the square array is analyzed to calculate and obtain the characteristic value reflecting the complexity of the shape of the kettle lid mold, thereby enhancing the subsequent detection of the deformation-prone area of ​​the kettle lid mold and improving the sensitivity of deformation detection; The present application determines the symmetric matrix of each matrix based on the position distribution of each matrix in its corresponding contour matrix; replaces all contour data in each sub-interval in each matrix with the value of the right endpoint of the sub-interval, and records all replaced data as quantized contour data; based on the distribution of all quantized contour data in each matrix and its symmetric matrix, and the similarity of all column vectors between each matrix and its opposite matrix, determines the symmetry coefficient between each matrix and its symmetric matrix, and combines the complexity of each matrix and its symmetric matrix to determine the distinction between each matrix and its symmetric matrix, which has the beneficial effect of enhancing the detection capability of subtle deformation in the complex contour area of ​​the kettle cover mold; at the same time, the contour features of the edge of the kettle cover mold are used to improve the subsequent detection sensitivity of the deformation of the edge of the kettle cover mold; The present application measures multiple times and obtains the degree of distinction between all square matrices and their respective symmetrical square matrices of each kettle lid mold, and based on the difference in the degree of distinction between all square matrices and their respective symmetrical square matrices between different measurements, determines the outlier value of each kettle lid mold, and inspects the kettle lid mold, thereby improving the accuracy of detecting areas prone to deformation and improving the sensitivity of detecting deformation of the kettle lid mold. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of a method for detecting deformation of a kettle cover mold provided by one embodiment of the present application; Figure 2 A schematic diagram of a process for constructing a profile matrix provided in one embodiment of the present application; Figure 3 A schematic diagram of a sub-interval division process provided in one embodiment of the present application; Figure 4 A schematic diagram of the distribution of quantized profile data of a kettle cover mold provided by one embodiment of the present application; Figure 5 A schematic diagram of the distribution of quantitative contour data of the mouth and bottom of a kettle cover mold provided by one embodiment of the present application; Figure 6 A flowchart of the distinguishability extraction steps provided for one embodiment of the present application; Figure 7 A block diagram of a kettle cover mold deformation detection system provided for one embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of a kettle cover mold deformation detection method, system and equipment proposed in the present application, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The following is a detailed description of a method, system and device for detecting deformation of a kettle cover mold provided by the present application in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a method for detecting deformation of a kettle cover mold provided by an embodiment of the present application, the method comprising the following steps: Step S1: Within a preset time, use a laser contour scanner to scan each kettle lid mold multiple times, measure the contour data of all scanning points in each scan, and form a contour data vector for each scan. The contour data vectors of all scans constitute a contour matrix of each kettle lid mold, and the contour matrix is ​​evenly divided into multiple square matrices, where the contour data is the distance from the laser beam emitted by the laser contour scanner to all scanning points.

[0021] Place the kettle cover mould on a horizontal measuring table controlled by a linear motor, install a laser profile scanner above the horizontal measuring table, make the measuring reference line of the laser profile scanner perpendicular to the axis direction of the kettle cover mould, and set the speed of the linear motor to v.

[0022] Within a preset time, a laser profile scanner is used to scan each kettle lid mold multiple times, and the profile data of all scanning points in each scan are obtained, and the profile data vector of each scan is formed, and the profile data vectors of all scans are used to form the profile matrix of each kettle lid mold in ascending order of scanning time, and the profile matrix is ​​evenly divided into M square arrays, and the number of rows and columns of each square array is W, wherein the scanning frequency of the laser scanner is set to f, and the number of profile data obtained in each scan is L. The preset time of measurement is t, and the profile data of the kettle lid mold is scanned and obtained, wherein the number of scans per second of the laser profile scanner is f, and L profile data are obtained in each scan, wherein the profile data is the distance from the laser beam emitted by the laser profile scanner to all scanning points, and since the central axis of the kettle lid mold is parallel to the central axis of the horizontal measuring platform and the projection of the central axis of the kettle lid mold coincides with the horizontal measuring platform, the profile data in the profile matrix is ​​symmetrical about the central axis of the kettle lid mold.

[0023] It should be noted that the speed v of the linear motor, the preset time t, the scanning frequency f, the number L of contour data obtained in each scan, the number M of square arrays, and the number W of square array rows and columns are all manually set. In this embodiment, the speed v of the linear motor is 0.3m / s, the preset time t is 1s, the scanning frequency f is 3000Hz, the number L of contour data obtained in each scan is 3000, the number M of square arrays is 50, and the number W of square array rows and columns is 100. The implementers can also set the data settings according to the specific circumstances, and this embodiment does not impose any special restrictions.

[0024] In addition, it should be understood that in the process of evenly dividing the contour matrix into multiple square matrices, if there are insufficient columns and rows to constitute square matrices of equal size, these columns and rows will not be considered, and the division of the square matrices is carried out in the order from left to right and from top to bottom according to the contour data in the contour matrix. The implementer may also adopt other division methods. This embodiment does not impose any special restrictions on the specific method of dividing the square matrix.

[0025] Preferably, the schematic diagram of the outline matrix construction process provided in this embodiment is as follows: Figure 2 shown.

[0026] Step S2: In each kettle lid mold, for any measurement, based on the extreme distribution of all contour data in each square matrix, determine the contour change interval of each square matrix, divide the contour change interval into multiple sub-intervals, and determine the complexity of each square matrix based on the proportion of all contour data located in each sub-interval in all contour data in the square matrix, and the degree of discreteness of the number of rows and columns of all contour data located in each sub-interval in each square matrix.

[0027] Since the kettle cover mold has more sharp corners and thinner walls in the area with greater contour complexity, the probability of deformation during injection molding is greater. It is necessary to obtain the distribution complexity of different square array contour data to reflect the probability of deformation of the kettle cover mold.

[0028] Therefore, based on the extreme distribution of all contour data in each square matrix, the contour change interval of each square matrix is ​​determined, and the contour change interval is evenly divided into multiple sub-intervals. Based on the proportion of all contour data in each sub-interval in each square matrix, and the degree of discreteness of the number of rows and columns of all contour data in each sub-interval in each square matrix, the complexity of each square matrix is ​​determined to enhance the subsequent detection of the deformation-prone area of ​​the kettle cover mold and improve the accuracy of deformation detection. Specifically: (1) In each kettle lid mold, for any measurement, the maximum and minimum values ​​of all contour data in each square matrix are used as the upper and lower limits of the contour change interval, respectively, and the contour data change interval is divided into K sub-intervals of equal length; It should be noted that the value of the number of sub-intervals K is artificially set. In this embodiment, the value of the number of sub-intervals K is 10. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions. Among them, the sub-interval is an interval that is open on the left and closed on the right.

[0029] Preferably, the schematic diagram of the sub-interval division process provided in this embodiment is as follows: Figure 3 shown.

[0030] (2) Further, based on the proportion of all contour data in each sub-interval in each square matrix and the degree of discreteness of the number of rows and columns of all contour data in each sub-interval in each square matrix, the complexity of each square matrix is ​​determined, specifically: The complexity of square matrix i The expression is: ; In the formula, , They represent the discrete degree of the number of rows and the discrete degree of the number of columns of all contour data in subinterval k in square matrix i respectively; It represents the proportion of all contour data in subinterval k in square matrix i to all contour data in square matrix i; represents the number of all subintervals in the contour variation interval of the square matrix i; ln() represents a logarithmic function with a natural constant as the base. It should be noted that there are many methods for measuring the degree of discreteness of a set of data. In this embodiment, the degree of discreteness of the number of rows and the number of columns of all contour data in the subinterval of the square matrix is ​​measured by calculating the coefficient of variation of the number of rows and the coefficient of variation of the number of columns. The implementer may also use other methods for measuring the degree of discreteness of data such as variance and standard deviation. Regarding the selection of the method for measuring the degree of discreteness of a set of data, this embodiment does not impose any special restrictions.

[0031] The calculation steps of the coefficient of variation are well-known techniques, and the specific calculation process will not be described in detail.

[0032] Further, according to the complexity of each square matrix, it can be understood that the greater the proportion of all contour data located in the subinterval of the square matrix in all contour data of the square matrix, the more complex the shape of the corresponding kettle lid mold part is, and the greater the calculated complexity is; the greater the degree of discreteness of the number of rows and columns of all contour data located in the subinterval of the square matrix, the more dispersed the position distribution of the contour data located in the same subinterval of the square matrix is, reflecting that the shape of the kettle lid mold is more complex and the corresponding area is more likely to deform, and the greater the calculated complexity is; conversely, the smaller the proportion of all contour data located in the subinterval of the square matrix in all contour data of the square matrix, the simpler the shape of the corresponding kettle lid mold part is, and the smaller the calculated complexity is; the smaller the degree of discreteness of the number of rows and columns of all contour data located in the subinterval of the square matrix, the more concentrated the position distribution of the contour data located in the same subinterval of the square matrix is, reflecting that the shape of the kettle lid mold is simpler and the corresponding area is less likely to deform, and the smaller the calculated complexity is.

[0033] Step S3: Based on the position distribution of each square matrix in its corresponding contour matrix, determine the symmetric square matrix of each square matrix; replace all contour data located in each sub-interval in each square matrix with the value of the right endpoint of the sub-interval, and record all the replaced data as quantized contour data; based on the distribution of all quantized contour data in each square matrix and its symmetric square matrix, and the similarity of all column vectors between each square matrix and its opposite square matrix, determine the symmetry coefficient between each square matrix and its symmetric square matrix, and combine the complexity of each square matrix and its symmetric square matrix to determine the distinction between each square matrix and its symmetric square matrix.

[0034] In the contour matrix of each kettle lid mold, the contour data is symmetrically distributed about the central axis of the kettle lid mold according to the shape of the kettle lid mold. If the square matrix D and the square matrix E are mirror-symmetrical about the central axis of the kettle lid mold, then the square matrix D and the square matrix E are mutually symmetrical in position, and the square matrix D is recorded as the symmetrical square matrix of the square matrix E (or the square matrix E is recorded as the symmetrical square matrix of the square matrix D). When a certain area in the mold is deformed, the similarity between the contour data of the corresponding square matrix and its symmetrical square matrix will decrease. In addition, the shape of the kettle lid mold is more complex, and the small threaded protrusions are easily deformed during demolding. Moreover, the mouth of the mold has a greater impact on the sealing performance of the kettle lid after injection molding, and it is necessary to strengthen its deformation detection. At the same time, near the mouth of the kettle lid mold, the distribution of the contour data in the square array changes greatly, and two relatively obvious peaks appear in the contour data. The closer the heights of the two peaks are, the closer the proportions of the corresponding contour data of the top and bottom of the kettle mouth in the square array are, and the contour data in the square array is more likely to be data measured at the mouth of the kettle lid mold. Preferably, the schematic diagram of the distribution of the quantized contour data of the kettle lid mold provided in this embodiment is as follows: Figure 4 As shown, Figure 4 The horizontal axis represents the quantitative contour data, in mm; the vertical axis represents the frequency, dimensionless. Figure 5 As shown, Figure 5 The horizontal axis represents the quantitative profile data in mm, while the vertical axis represents the frequency, which is dimensionless.

[0035] Therefore, based on the distribution of all quantized contour data in each matrix and its symmetric matrix, and the similarity of all column vectors between each matrix and its symmetric matrix, the symmetry coefficient between each matrix and its symmetric matrix is ​​determined, and combined with the complexity of each matrix and its symmetric matrix, the distinction between each matrix and its symmetric matrix is ​​determined to improve the detection sensitivity of the deformation of the edge of the kettle lid mold. Specifically: (1) Replace all contour data in each sub-interval in each square matrix with the value of the upper limit of the sub-interval, and record all the replaced data as quantized contour data; All quantized contour data of each square matrix are used as the input of the maximum inter-class variance algorithm, and the maximum inter-class variance of each square matrix is ​​output; Among them, the maximum inter-class variance algorithm is a well-known technology, and the specific process of calculating the maximum inter-class variance is not repeated here.

[0036] (2) Further, based on the distribution of all quantized profile data in each matrix and its symmetric matrix, and the similarity of all column vectors between each matrix and its symmetric matrix, the symmetry coefficient between each matrix and its symmetric matrix is ​​determined, specifically: Symmetry coefficient between matrix i and its symmetric matrix The expression is: ; In the formula, It represents the sum of the maximum between-class variance of matrix i and the maximum between-class variance of the symmetric matrix of matrix i; Represents the jth column vector of square matrix i and the jth column vector of the symmetric square matrix of square matrix i The similarity between column vectors; W represents the total number of columns in each square matrix; norm() represents the normalization function.

[0037] It should be understood that the norm normalization here is to normalize the sum of the maximum inter-class variance of square matrix i and the maximum inter-class variance of the symmetric matrix of square matrix i. Each square matrix has a , and normalize it. In addition, there are many commonly used normalization functions. The linear normalization method is used in this embodiment. The implementer can also use other normalization methods such as the maximum and minimum value normalization method. Regarding the selection of the normalization method, this embodiment does not make any special restrictions.

[0038] Among them, the linear normalization method is a well-known technology, and the specific process of normalizing the data is not repeated here.

[0039] It should be noted that there are many methods for measuring the similarity between vectors. In this embodiment, the similarity between the jth column vector of the square matrix i and the jth column vector of the symmetric square matrix i is calculated. The cosine similarity between column vectors is used to measure the similarity between column vectors. The implementer may also use other methods that can measure the similarity between vectors, such as the inverse of the Euclidean distance. This embodiment does not impose any special restrictions on the selection of the method for measuring the similarity between vectors.

[0040] The calculation process of cosine similarity is a well-known technology, and its specific calculation steps are not repeated here.

[0041] Furthermore, according to the symmetry coefficient between each square matrix and its symmetric square matrix, it can be understood that the greater the similarity between the square matrix and the column vectors in its symmetric square matrix that are symmetrical about the axis of the kettle cover mold, the greater the degree of symmetry between the two, and the larger the calculated symmetry coefficient; when the mold area corresponding to the square matrix is ​​deformed, the symmetry coefficient will become smaller, thereby detecting the mold deformation. In addition, in order to strengthen the deformation detection of the edge of the mold mouth, the sum of the maximum inter-class variances of the square matrix and the symmetric square matrix is ​​used as the weight, and the larger the sum of the maximum inter-class variances is, the greater the probability that the square matrix is ​​at the edge of the pot mouth, and the larger the calculated symmetry coefficient is; at the same time, when the pot mouth edge of the kettle cover mold is deformed, the change in the symmetry coefficient is large. The contour features of the pot mouth edge of the kettle cover mold are used to amplify the change in the symmetry coefficient when deformation occurs, thereby improving the subsequent detection sensitivity of the deformation of the pot mouth edge of the kettle cover mold; conversely, the smaller the similarity between the square matrix and the column vectors in its symmetric square matrix that are symmetrical about the axis of the kettle cover mold, the smaller the degree of symmetry between the two, and the smaller the calculated symmetry coefficient; the smaller the sum of the maximum inter-class variances of the square matrix and the symmetric square matrix, the smaller the calculated symmetry coefficient.

[0042] (3) Further, the sum of the complexity of each square matrix and its symmetric square matrix is ​​calculated, and the ratio of the sum to the symmetry coefficient is used as the discrimination between each square matrix and its symmetric square matrix.

[0043] According to the discrimination between each square matrix and its symmetrical square matrix, it can be understood that when the kettle cover mold is deformed, the similarity between the square matrix and its symmetrical square matrix contour data decreases, the symmetry coefficient decreases, and the calculated discrimination becomes larger. At the same time, when the square matrix and its symmetrical square matrix reflect the complexity of the shape of the kettle cover mold, the greater the probability of deformation in the corresponding area of ​​the kettle cover mold, the greater the discrimination obtained by calculation, and then when the corresponding area of ​​the mold is deformed, the change in discrimination is large, which is convenient for subsequent timely detection of mold deformation; on the contrary, when the kettle cover mold is not deformed, the similarity between the square matrix and its symmetrical square matrix contour data increases, the symmetry coefficient increases, and the calculated discrimination becomes smaller. At the same time, when the square matrix and its symmetrical square matrix reflect the complexity of the shape of the kettle cover mold, the smaller the probability of deformation in the corresponding area of ​​the kettle cover mold, and the smaller the discrimination obtained by calculation.

[0044] Preferably, the flowchart of the discrimination extraction step provided in this embodiment is as follows: Figure 6 shown.

[0045] Step S4: perform multiple measurements and obtain the degree of distinction between all square matrices of each kettle lid mold and its respective symmetrical square matrices under different measurements, and determine the abnormal value of each kettle lid mold based on the difference in the degree of distinction between all square matrices and their symmetrical square matrices between different measurements, and detect the kettle lid mold.

[0046] In order to improve the accuracy of the kettle cover mold detection, steps S1-S3 are performed multiple times to obtain the discrimination between all square arrays and their respective symmetrical square arrays in each kettle cover mold under multiple measurements, and based on the difference between the discrimination between all square arrays and their respective symmetrical square arrays in different measurements, the outlier value of each kettle cover mold is determined, specifically: For the sake of ease of expression, the discrimination between each matrix and its symmetrical matrix is ​​simply recorded as the discrimination of each matrix. In each kettle lid mold, the difference between the discrimination of any two matrixes in all measurements is calculated, which is recorded as the inter-matrix difference between any two matrixes. The inter-matrix differences between all pairwise combinations are taken as the normalized result and taken as the outlier of each kettle lid mold.

[0047] It should be noted that there are many methods for measuring the differences between data groups. In this embodiment, the DTW distance between the discrimination of any two matrices in all measurements is calculated to measure the difference between the discrimination of any two matrices and their corresponding matrices in all measurements. The implementer may also use other methods for measuring the differences between data groups, such as Euclidean distance and Manhattan distance. This embodiment does not impose any special restrictions on the selection of methods for measuring the differences between data groups.

[0048] The calculation process of the DTW distance is a well-known technology, and its specific calculation steps are not repeated here. In this embodiment, the number of measurements is set to 50, and the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0049] The greater the difference in the degree of distinction between all square matrices in each kettle lid mold and its symmetric square matrices between all measurements, the greater the probability that the kettle lid mold will be deformed, and the larger the calculated outlier value; conversely, the smaller the difference in the degree of distinction between all square matrices in each kettle lid mold and its symmetric square matrices between all measurements, the smaller the probability that the kettle lid mold will be deformed, and the smaller the calculated outlier value.

[0050] Furthermore, the mean of the outlier values ​​of all kettle cover molds is used as the deformation threshold. If the outlier value of the kettle cover mold is greater than the deformation threshold, the kettle cover mold is deformed. Otherwise, if the outlier value of the kettle cover mold is greater than the deformation threshold and less than or equal to the deformation threshold, the kettle cover mold is not deformed.

[0051] Based on the same inventive concept as the above method, the embodiment of the present application also provides a kettle cover mold deformation detection system, including: The kettle cover mold data acquisition module is used to use a laser profile scanner to scan each kettle cover mold multiple times within a preset time, measure the profile data of all scanning points in each scan, and form a profile data vector for each scan, and the profile data vectors of all scans constitute a profile matrix of each kettle cover mold, and evenly divide the profile matrix into multiple square matrices, wherein the profile data is the distance from the laser beam emitted by the laser profile scanner to all scanning points; A kettle cover mold weight acquisition module is used to determine the contour change interval of each square matrix in each kettle cover mold based on the maximum value of all contour data in each square matrix, evenly divide the contour change interval into multiple sub-intervals of equal length, and determine the complexity of each square matrix based on the proportion of all contour data in each sub-interval in each square matrix in all contour data in the square matrix, and the discrete degree of the number of rows and columns of all contour data in each sub-interval in each square matrix; Based on the position distribution of each square matrix in its corresponding contour matrix, determine the square matrix that is positionally symmetric with each square matrix; replace all contour data in each sub-interval in each square matrix with the value of the upper limit of the sub-interval, record all replaced data as quantized contour data, and based on the distribution of all quantized contour data in each square matrix and its symmetric square matrix, and the similarity of all column vectors between each square matrix and its opposite square matrix, determine the symmetry coefficient between each square matrix and its symmetric square matrix, and combine the complexity of each square matrix and its symmetric square matrix to determine the discrimination between each square matrix and its symmetric square matrix; The kettle cover mold detection module is used to determine the abnormal value of each kettle cover mold based on the difference between the discrimination between all square arrays and their symmetrical square arrays in different measurements, and detect the kettle cover mold.

[0052] A block diagram of a kettle cover mold deformation detection system provided in an embodiment of the present application is as follows: Figure 7 shown.

[0053] Based on the same inventive concept as the above method, an embodiment of the present application also provides a kettle cover mold deformation detection device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned kettle cover mold deformation detection methods are implemented.

[0054] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0055] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0056] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting deformation of a kettle cover mold, characterized in that: The method comprises the following steps: Within a preset time, each kettle lid mold is scanned multiple times by using a laser profile scanner, the profile data of all scanning points in each scan are measured, and the profile data vectors of each scan are formed, the profile data vectors of all scans form a profile matrix of each kettle lid mold, and the profile matrix is ​​evenly divided into multiple square matrices, wherein the profile data is the distance from the laser beam emitted by the laser profile scanner to all scanning points; In each kettle lid mold, based on the maximum value of all contour data in each square matrix, the contour change interval of each square matrix is ​​determined, and the contour change interval is evenly divided into multiple sub-intervals of equal length. Based on the proportion of all contour data in each sub-interval in each square matrix in all contour data in the square matrix, and the degree of discreteness of the number of rows and columns of all contour data in each sub-interval in each square matrix, the complexity of each square matrix is ​​determined; Based on the position distribution of each square matrix in its corresponding contour matrix, determine the square matrix that is positionally symmetrical with each square matrix, and record the square matrix that is positionally symmetrical with each square matrix as the symmetric square matrix of each square matrix; replace all contour data in each sub-interval of each square matrix with the value of the upper limit of the sub-interval, record all the replaced data as quantized contour data, and based on the distribution of all quantized contour data in each square matrix and its symmetric square matrix, and the similarity of all column vectors between each square matrix and its opposite square matrix, determine the symmetry coefficient between each square matrix and its symmetric square matrix, and combine the complexity of each square matrix and its symmetric square matrix to determine the discrimination between each square matrix and its symmetric square matrix; Based on the difference in the degree of distinction between all square arrays and their symmetrical square arrays in different measurements, the outlier value of each kettle lid mold is determined and the kettle lid mold is tested.

2. A method for detecting deformation of a kettle cover mold as claimed in claim 1, characterized in that: The method for determining the contour change interval of each square matrix is: The maximum and minimum values ​​of all contour data in each square matrix are used as the upper and lower limits of the contour change range respectively.

3. A kettle cover mold deformation detection method as claimed in claim 1, characterized in that: The expression of the complexity of each square matrix is: ; In the formula, represents the complexity of square matrix i; , They represent the discrete degree of the number of rows and the discrete degree of the number of columns of all contour data in subinterval k in square matrix i respectively; It represents the proportion of all contour data in subinterval k in square matrix i to all contour data in square matrix i; represents the number of all subintervals in the contour change interval of square matrix i; ln( ) represents the logarithmic function with a natural constant as the base.

4. A kettle cover mold deformation detection method as claimed in claim 1, characterized in that: The step of determining a square matrix that is positionally symmetrical to each square matrix comprises: In the contour matrix of each kettle lid mould, the contour data are symmetrically distributed about the central axis of the kettle lid mould according to the shape of the kettle lid mould. If the square matrix D and the square matrix E are mirror-symmetrical about the central axis of the kettle lid mould, then the square matrix D and the square matrix E are positionally symmetrical to each other.

5. A kettle cover mold deformation detection method as claimed in claim 1, characterized in that: The method for determining the symmetry coefficient between each square matrix and its symmetric square matrix is: All quantized contour data of each square matrix are used as the input of the maximum inter-class variance algorithm, and the maximum inter-class variance of each square matrix is ​​output; Symmetry coefficient between matrix i and its symmetric matrix The expression is: ; In the formula, It represents the sum of the maximum between-class variance of matrix i and the maximum between-class variance of the symmetric matrix of matrix i; Represents the jth column vector of square matrix i and the jth column vector of the symmetric square matrix of square matrix i The similarity between column vectors; W represents the total number of columns in each square matrix; norm() represents the normalization function.

6. A kettle cover mold deformation detection method as claimed in claim 1, characterized in that: The method for determining the degree of distinction between each square matrix and its symmetric square matrix is: The sum of the complexity of each square matrix and its symmetric square matrix is ​​calculated, and the ratio of the sum to the symmetry coefficient is used as the discrimination between each square matrix and its symmetric square matrix.

7. A method for detecting deformation of a kettle cover mold as claimed in claim 1, characterized in that: The outlier value of each kettle cover mold is a normalized result of taking the average of the differences between all measurements of the discrimination between all square matrices and their symmetric square matrices in each kettle cover mold.

8. A method for detecting deformation of a kettle cover mold as claimed in claim 1, characterized in that: The process of detecting the kettle cover mold is as follows: The mean of the outliers of all kettle cover molds is taken as the deformation threshold. If the outlier value of the kettle cover mold is greater than the deformation threshold, the kettle cover mold is deformed; otherwise, the kettle cover mold is not deformed.

9. A kettle cover mold deformation detection system, implementing a kettle cover mold deformation detection method as claimed in any one of claims 1 to 8, characterized in that: The system comprises: The kettle cover mold data acquisition module is used to use a laser profile scanner to scan each kettle cover mold multiple times within a preset time, measure the profile data of all scanning points in each scan, and form a profile data vector for each scan, and the profile data vectors of all scans constitute a profile matrix of each kettle cover mold, and evenly divide the profile matrix into multiple square matrices, wherein the profile data is the distance from the laser beam emitted by the laser profile scanner to all scanning points; A kettle cover mold weight acquisition module is used to determine the contour change interval of each square matrix in each kettle cover mold based on the maximum value of all contour data in each square matrix, evenly divide the contour change interval into multiple sub-intervals of equal length, and determine the complexity of each square matrix based on the proportion of all contour data in each sub-interval in each square matrix in all contour data in the square matrix, and the discrete degree of the number of rows and columns of all contour data in each sub-interval in each square matrix; Based on the position distribution of each square matrix in its corresponding contour matrix, determine the square matrix that is positionally symmetrical with each square matrix, and record the square matrix that is positionally symmetrical with each square matrix as the symmetric square matrix of each square matrix; replace all contour data in each sub-interval of each square matrix with the value of the upper limit of the sub-interval, record all the replaced data as quantized contour data, and based on the distribution of all quantized contour data in each square matrix and its symmetric square matrix, and the similarity of all column vectors between each square matrix and its opposite square matrix, determine the symmetry coefficient between each square matrix and its symmetric square matrix, and combine the complexity of each square matrix and its symmetric square matrix to determine the discrimination between each square matrix and its symmetric square matrix; The kettle cover mold detection module is used to determine the abnormal value of each kettle cover mold based on the difference between the distinction between all square arrays and their symmetrical square arrays in different measurements, and detect the kettle cover mold.

10. A kettle cover mold deformation detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a kettle cover mold deformation detection method as described in any one of claims 1-8 are implemented.

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