Method and device for detecting depth of strip edge waviness defects

Abstract

The present invention discloses a method and device for detecting the depth of strip edge waviness-like defects, which relates to the technical field of strip edge wave depth evaluation. The method includes: obtaining a three-dimensional cloud map of the strip shape height distribution and strip shape height point cloud data, identifying the edge waviness-like defects in the three-dimensional cloud map of the height distribution, determining the target area corresponding to the edge waviness-like defects, extracting the defect area height point cloud data corresponding to the target area from the strip shape height point cloud data, determining the total number of columns of the height point cloud data array included between the target side edge of the strip and the first target array, obtaining the data point spacing in the strip shape height point cloud data, and determining the depth of the edge waviness-like defects according to the total number of columns and the data point spacing. The present invention can accurately detect the depth of strip edge waviness-like defects, which is beneficial to accurately evaluating the strip shape quality.

Description

Technical Field

[0001] The present invention relates to the technical field of strip edge wave depth evaluation, and particularly to a method and device for detecting the depth of strip edge wave type defects. Background Art

[0002] The quality of strip shape directly affects the use of subsequent processes by customers. The strip edge shape defects to a certain extent determine the strip shape quality. The larger the strip edge shape defects, the worse the strip shape quality. The strip edge shape defects are mainly edge wave type defects. The depth of the edge wave type defects reflects the size of the edge wave type defects and is an important index for evaluating the strip shape quality. Therefore, accurately detecting the depth of strip edge wave type defects is of great significance for evaluating the strip shape quality. Summary of the Invention

[0003] The present invention provides a method and device for detecting the depth of strip edge wave type defects, and solves the technical problem of how to detect the depth of strip edge wave type defects.

[0004] On the one hand, the embodiments of the present invention provide the following technical solutions:

[0005] A method for detecting the depth of strip edge wave type defects, comprising:

[0006] Obtaining a three-dimensional cloud map of strip shape height distribution and strip shape height point cloud data, where the strip shape height point cloud data includes multiple columns of height point cloud data arrays, and each column of the height point cloud data array includes height data of multiple data points;

[0007] Identifying edge wave type defects in the three-dimensional cloud map of height distribution;

[0008] Determining a target area corresponding to the edge wave type defect, and extracting defect area height point cloud data corresponding to the target area from the strip shape height point cloud data;

[0009] Determining the total number of columns of the height point cloud data arrays included between the target side edge of the strip and the first target array; the target side is the side of the strip where the target area is located, the first target array is the second target array that is the farthest from the target side edge in the defect area height point cloud data, and the second target array is at least one of the height point cloud data arrays in which the height data is greater than a preset height threshold;

[0010] Obtaining the data point spacing in the strip shape height point cloud data, and determining the depth of the edge wave type defect according to the total number of columns and the data point spacing.

[0011] Preferably, before identifying the edge waviness - like defects in the three - dimensional cloud map of height distribution and determining the total number of columns of the height point cloud data array included between the target side edge of the strip and the first target array, the following steps are further included:

[0012] Determine the preset height threshold corresponding to the edge waviness - like defects.

[0013] Preferably, determining the preset height threshold corresponding to the edge waviness - like defects includes:

[0014] If the edge waviness - like defect is an edge wave defect or an extended horseshoe - print defect, determine the preset height threshold to be 0.6;

[0015] If the edge waviness - like defect is a horseshoe - print defect, determine the preset height threshold to be 0.5;

[0016] If the edge waviness - like defect is a flanging defect, determine the preset height threshold to be 0.8.

[0017] Preferably, determining the depth of the edge waviness - like defects according to the total number of columns and the data point spacing includes:

[0018] Determine the weighting coefficient corresponding to the edge waviness - like defects;

[0019] Determine the depth of the edge waviness - like defects according to the weighting coefficient, the total number of columns and the data point spacing.

[0020] Preferably, determining the weighting coefficient corresponding to the edge waviness - like defects includes:

[0021] If the edge waviness - like defect is an edge wave defect, determine the weighting coefficient to be 1.2;

[0022] If the edge waviness - like defect is a horseshoe - print defect, determine the weighting coefficient to be 1.6;

[0023] If the edge waviness - like defect is an extended horseshoe - print defect, determine the weighting coefficient to be 1.5;

[0024] If the edge waviness - like defect is a flanging defect, determine the weighting coefficient to be 1.0.

[0025] Preferably, determining the depth of the edge waviness - like defects according to the total number of columns and the data point spacing includes:

[0026] W = k×(L - 1)×e; where W is the depth of the edge waviness - like defect, k is the weighting coefficient, L is the total number of columns, and e is the data point spacing.

[0027] Preferably, the data point spacing is 0.88 mm.

[0028] On the other hand, the embodiments of the present invention also provide the following technical solutions:

[0029] A strip edge waviness type defect depth detection device, comprising:

[0030] An image data acquisition module, configured to acquire a three-dimensional cloud map of the strip shape height distribution and strip shape height point cloud data, where the strip shape height point cloud data includes multiple columns of height point cloud data arrays, and each column of the height point cloud data array includes height data of multiple data points;

[0031] A shape defect identification module, configured to identify edge waviness type defects in the three-dimensional cloud map of the height distribution;

[0032] A defect data extraction module, configured to determine a target area corresponding to the edge waviness type defect, and extract defect area height point cloud data corresponding to the target area from the strip shape height point cloud data;

[0033] A total column number determination module, configured to determine the total number of columns of the height point cloud data arrays included between the target side edge of the strip and the first target array; the target side is one side of the strip where the target area is located, the first target array is the second target array that is the farthest from the target side edge in the defect area height point cloud data, and the second target array is at least one of the height point cloud data arrays in which the height data is greater than a preset height threshold;

[0034] A defect depth determination module, configured to obtain the data point spacing in the strip shape height point cloud data, and determine the depth of the edge waviness type defect according to the total number of columns and the data point spacing.

[0035] On the other hand, the embodiments of the present invention also provide the following technical solutions:

[0036] An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the program, it implements any of the above strip edge waviness type defect depth detection methods.

[0037] On the other hand, the embodiments of the present invention also provide the following technical solutions:

[0038] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements any of the above strip edge waviness type defect depth detection methods.

[0039] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0040] The present invention obtains a three-dimensional cloud map of the strip shape height distribution and strip shape height point cloud data, identifies edge waviness defects in the three-dimensional cloud map of the height distribution, determines the target area corresponding to the edge waviness defects, extracts the defect area height point cloud data corresponding to the target area from the strip shape height point cloud data, determines the total number of columns of the height point cloud data array included between the target side edge of the strip and the first target array, obtains the data point spacing in the strip shape height point cloud data, and determines the depth of the edge waviness defects according to the total number of columns and the data point spacing. It can accurately detect the depth of the edge waviness defects of the strip, which is beneficial to accurately evaluating the strip shape quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following described drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0042] Figure 1 It is a flowchart of the method for detecting the depth of edge waviness defects of the strip in the embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of the three-dimensional cloud map of the strip shape height distribution in the embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram of the strip shape height point cloud data in the embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of the edge waviness defects in the embodiment of the present invention;

[0046] Figure 5 It is a schematic diagram of the device for detecting the depth of edge waviness defects of the strip in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The embodiments of the present invention provide a method and device for detecting the depth of edge waviness defects of the strip, and solve the technical problem of how to detect the depth of edge waviness defects of the strip.

[0048] To better understand the technical solutions of the present invention, the following will specifically describe the technical solutions of the present invention in detail in conjunction with the drawings in the specification and the specific embodiments.

[0049] As Figure 1 shown, the method for detecting the depth of edge waviness defects of the strip in this embodiment includes:

[0050] Step S1: Obtain the three-dimensional cloud map of the strip shape height distribution and the strip shape height point cloud data. The strip shape height point cloud data includes multiple columns of height point cloud data arrays, and each column of height point cloud data array includes the height data of multiple data points.

[0051] Step S2: Identify the edge waviness type defects in the three-dimensional cloud map of the height distribution.

[0052] Step S3: Determine the target area corresponding to the edge waviness type defects, and extract the defect area height point cloud data corresponding to the target area from the strip shape height point cloud data.

[0053] Step S4: Determine the total number of columns of the height point cloud data arrays included between the target side edge of the strip and the first target array. The target side is the side of the strip where the target area is located, the first target array is the second target array that is the farthest from the target side edge in the defect area height point cloud data, and the second target array is a height point cloud data array with at least one height data greater than the preset height threshold.

[0054] Step S5: Obtain the data point spacing in the strip shape height point cloud data, and determine the depth of the edge waviness type defects according to the total number of columns and the data point spacing.

[0055] In Step S1, the three-dimensional cloud map of the strip shape height distribution is as Figure 2 shown, and the strip shape height point cloud data is as Figure 3 shown. The working side of the strip is the side close to the operating room in the width direction of the strip, and the driving side of the strip is the side far from the operating room in the width direction of the strip. The height point cloud data arrays are such as the arrays with column numbers "0", "1"... "x + 3", "x + 4", etc. on the working side in Figure 3 . The data points are points at different positions of the strip, and the height data of the data points are, for example, 0.5 / 0.8 / 0.7, etc. in the array with the working side column number "0". The position of the height data in the strip shape height point cloud data is the same as the position of the data point on the strip.

[0056] In Step S2, the edge waviness type defects identified in the three-dimensional cloud map of the height distribution are as Figure 4 shown. The edge waviness type defects can be edge wave defects, horseshoe mark defects, horseshoe mark defects with extension, or flanging defects, etc.

[0057] In Step S3, the target area is the area occupied by the edge waviness type defects on the strip, and the defect area height point cloud data corresponding to the target area is the array A on the working side or the driving side in Figure 3 .

[0058] In step S4, the target side can be the working side or the driving side; the preset height threshold can be a fixed value, such as 0.6, and different preset height thresholds can also be adopted for different types of edge waviness defects. If the target area is located on the working side of the strip, then for Figure 3 the height point cloud data (array A) of the defective area on the working side in

[0059] In this embodiment, the data points are evenly distributed on the strip, that is, the distance between two adjacent data points in the width direction and the length direction is the same. Therefore, the height data of the data points is also evenly distributed in the strip shape height point cloud data. In step S5, the data point spacing in the strip shape height point cloud data refers to the distance between the data points corresponding to the two height data in the same row in two adjacent column height point cloud data arrays on the strip. For example, Figure 3 the distance between the data points corresponding to the two height data 0.5 and 0.5 in the first row of the height point cloud data arrays with column numbers 0 and 1 in

[0060] In step S5, the formula for determining the depth of the edge waviness defect according to the total number of columns and the data point spacing is: W = k×(L - 1)×e; W is the depth of the edge waviness defect, k is the weighting coefficient, L is the total number of columns, and e is the data point spacing. For example, Figure 3 for array A on the working side in

[0061] As can be seen from the above, in this embodiment, a three-dimensional cloud map of the strip shape height distribution and strip shape height point cloud data are obtained, edge wave-like defects in the three-dimensional cloud map of the height distribution are identified, a target area corresponding to the edge wave-like defects is determined, defect area height point cloud data corresponding to the target area is extracted from the strip shape height point cloud data, the total number of columns of the height point cloud data array included between the target side edge of the strip and the first target array is determined, the data point spacing in the strip shape height point cloud data is obtained, and the depth of the edge wave-like defects is determined according to the total number of columns and the data point spacing. The depth of the edge wave-like defects of the strip can be accurately detected, which is beneficial to accurately evaluating the strip shape quality.

[0062] In this embodiment, the preset height threshold is the critical value for determining whether the position of the data point is a defect. If the same preset height threshold is used for different types of edge wave-like defects, it will lead to an unreasonable determination of the second target array, and ultimately result in inaccurate depth of the edge wave-like defects. For this reason, preferably after step S2 and before step S4 in this embodiment, the strip edge wave-like defect depth detection method further includes:

[0063] Determining a preset height threshold corresponding to the edge wave-like defects.

[0064] Specifically, determining a preset height threshold corresponding to the edge wave-like defects includes: if the edge wave-like defect is an edge wave defect or a horseshoe print defect with extension, then determining the preset height threshold to be 0.6; if the edge wave-like defect is a horseshoe print defect, then determining the preset height threshold to be 0.5; if the edge wave-like defect is a flanging defect, then determining the preset height threshold to be 0.8. The preset height threshold set in this way is suitable for the type of edge wave-like defects, and the depth of the edge wave-like defects obtained is more accurate.

[0065] In this embodiment, in the formula for calculating the depth of the edge wave-like defects, the weighting coefficient is used to accurately correct the depth of the edge wave-like defects. If the same weighting coefficient is used for different types of edge wave-like defects, the depth of the edge wave-like defects may not be accurately corrected. For this reason, preferably in step S5 of this embodiment, determining the depth of the edge wave-like defects according to the total number of columns and the data point spacing includes:

[0066] Determining a weighting coefficient corresponding to the edge wave-like defects;

[0067] Determining the depth of the edge wave-like defects according to the weighting coefficient, the total number of columns, and the data point spacing.

[0068] Specifically, determining the weighting coefficient corresponding to the edge waviness type defect includes: if the edge waviness type defect is an edge wave defect, determining the weighting coefficient to be 1.2; if the edge waviness type defect is a horseshoe print defect, determining the weighting coefficient to be 1.6; if the edge waviness type defect is an extended horseshoe print defect, determining the weighting coefficient to be 1.5; if the edge waviness type defect is a flanging defect, determining the weighting coefficient to be 1.0. The weighting coefficient set in this way is suitable for the type of edge waviness type defect, and the depth of the edge waviness type defect obtained is more accurate.

[0069] As Figure 5 shown, this embodiment also provides a device for detecting the depth of strip edge waviness type defects, including:

[0070] An image data acquisition module for acquiring a three-dimensional cloud map of the strip shape height distribution and strip shape height point cloud data. The strip shape height point cloud data includes multiple columns of height point cloud data arrays, and each column of height point cloud data array includes height data of multiple data points;

[0071] A shape defect identification module for identifying edge waviness type defects in the three-dimensional cloud map of height distribution;

[0072] A defect data extraction module for determining the target area corresponding to the edge waviness type defect and extracting the defect area height point cloud data corresponding to the target area from the strip shape height point cloud data;

[0073] A total column number determination module for determining the total number of columns of height point cloud data arrays included between the target side edge of the strip and the first target array; the target side is the side of the strip where the target area is located, the first target array is the second target array that is the farthest from the target side edge in the defect area height point cloud data, and the second target array is a height point cloud data array with at least one height data greater than a preset height threshold;

[0074] A defect depth determination module for obtaining the data point spacing in the strip shape height point cloud data and determining the depth of the edge waviness type defect according to the total number of columns and the data point spacing.

[0075] The device for detecting the depth of strip edge waviness type defects in this embodiment can accurately detect the depth of strip edge waviness type defects, which is beneficial to accurately evaluating the strip shape quality.

[0076] Based on the same inventive concept as the method for detecting the depth of strip edge waviness type defects described above, this embodiment also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of any one of the methods for detecting the depth of strip edge waviness type defects described above.

[0077] Among them, for the bus architecture (represented by the bus), the bus may include any number of interconnected buses and bridges, which link together various circuits including one or more processors represented by the processor and the memory represented by the memory. The bus may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and thus will not be further described herein. The bus interface provides an interface between the bus and the receiver and transmitter. The receiver and transmitter may be the same element, i.e., the transceiver, which provides a unit for communicating with various other devices on the transmission medium. The processor is responsible for managing the bus and general processing, while the memory may be used to store data used by the processor when performing operations.

[0078] Since the electronic device introduced in this embodiment is the electronic device used for implementing the strip edge waviness defect depth detection method in the embodiments of the present invention, based on the strip edge waviness defect depth detection method introduced in the embodiments of the present invention, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present invention will not be described in detail herein. As long as those skilled in the art implement the electronic device used for the strip edge waviness defect depth detection method in the embodiments of the present invention, it falls within the scope of protection of the present invention.

[0079] Based on the same inventive concept as the above strip edge waviness defect depth detection method, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program realizes any of the above strip edge waviness defect depth detection methods when executed by a processor.

[0080] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0081] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the specified functions in the process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0082] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the specified functions in the process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in the process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0084] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for detecting the depth of strip edge waviness - like defects, characterized in that, Comprising: Obtaining a three-dimensional cloud map of the strip shape height distribution and strip shape height point cloud data of the strip, where the strip shape height point cloud data includes multiple columns of height point cloud data arrays, and each column of the height point cloud data arrays includes height data of multiple data points; Identifying edge waviness type defects in the three-dimensional cloud map of the height distribution; Determining a target area corresponding to the edge waviness type defect, and extracting defect area height point cloud data corresponding to the target area from the strip shape height point cloud data of the strip; Determining the total number of columns of the height point cloud data arrays included between the target side edge of the strip and the first target array; the target side is one side of the strip where the target area is located, and the first target array is the second target array that is the farthest from the target side edge in the defect area height point cloud data, and the second target array is the height point cloud data array in which at least one of the height data is greater than a preset height threshold; Obtaining the data point spacing in the strip shape height point cloud data of the strip, and determining the depth of the edge waviness type defect according to the total number of columns and the data point spacing.

2. The method for detecting the depth of strip edge waviness - like defects according to claim 1, characterized in that, Before determining the total number of columns of the height point cloud data arrays included between the target side edge of the strip and the first target array after identifying the edge waviness type defects in the three-dimensional cloud map of the height distribution, it further includes: Determining the preset height threshold corresponding to the edge waviness type defect.

3. The method for detecting the depth of strip edge waviness - like defects according to claim 2, characterized in that, Determining the preset height threshold corresponding to the edge waviness type defect includes: If the edge waviness type defect is an edge wave defect or an extended horseshoe print defect, determining the preset height threshold to be 0.6; If the edge waviness type defect is a horseshoe print defect, determining the preset height threshold to be 0.5; If the edge waviness type defect is a flanging defect, determining the preset height threshold to be 0.

8.

4. The method for detecting the depth of strip edge waviness - like defects according to claim 1, characterized in that, Determining the depth of the edge waviness type defect according to the total number of columns and the data point spacing includes: Determining the weighting coefficient corresponding to the edge waviness type defect; Determining the depth of the edge waviness type defect according to the weighting coefficient, the total number of columns, and the data point spacing.

5. The method for detecting the depth of strip edge waviness - like defects according to claim 4, characterized in that, Determining the weighting coefficient corresponding to the edge waviness type defect includes: If the edge waviness type defect is an edge wave defect, determining the weighting coefficient to be 1.2; If the edge waviness type defect is a horseshoe print defect, determining the weighting coefficient to be 1.6; If the edge waviness type defect is an extended horseshoe print defect, determining the weighting coefficient to be 1.5; If the edge waviness type defect is a flanging defect, determining the weighting coefficient to be 1.

0.

6. The method for detecting the depth of strip edge waviness - like defects according to any one of claims 1 - 5, characterized in that, Determining the depth of the edge waviness type defect according to the total number of columns and the data point spacing includes: W = k×(L - 1)×e; W is the depth of the edge waviness type defect, k is the weighting coefficient, L is the total number of columns, and e is the data point spacing.

7. The method for detecting the depth of strip edge waviness - like defects according to claim 6, characterized in that, The data point spacing is 0.88 mm.

8. A device for detecting the depth of strip edge waviness - like defects, characterized in that, Comprising: An image data acquisition module, configured to obtain a three-dimensional cloud map of the strip shape height distribution and strip shape height point cloud data of the strip, where the strip shape height point cloud data includes multiple columns of height point cloud data arrays, and each column of the height point cloud data arrays includes height data of multiple data points; The shape defect recognition module is used to recognize the edge waviness defects in the three-dimensional cloud map of the height distribution; The defect data extraction module is used to determine the target area corresponding to the edge waviness defects, and extract the defect area height point cloud data corresponding to the target area from the strip shape height point cloud data; The total column number determination module is used to determine the total column number of the height point cloud data arrays included between the target side edge of the strip and the first target array; the target side is the side of the strip where the target area is located, and the first target array is the second target array in the defect area height point cloud data that is farthest from the target side edge, and the second target array is an array of the height point cloud data in which at least one of the height data is greater than a preset height threshold; The defect depth determination module is used to obtain the data point spacing in the strip shape height point cloud data, and determine the depth of the edge waviness defects according to the total column number and the data point spacing.

9. An electronic device, characterized in that It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the strip edge waviness defect depth detection method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements the strip edge waviness defect depth detection method according to any one of claims 1-7.

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