Method, system and device for realizing workpiece positioning in numerical control large glass edge grinding process, processor and computer readable storage medium thereof

Abstract

The invention relates to a method for realizing workpiece positioning in a numerical control large glass edging process, which comprises the following steps of: acquiring an image through a camera, and preprocessing the image; camera calibration is carried out, and internal parameters and external parameters of a camera are obtained; and converting an image coordinate system and a machine tool coordinate system. The invention further relates to a system and device for achieving workpiece positioning in the numerical control large glass edge grinding process, a processor and a readable storage medium of the processor. By adopting the method, the system, the device, the processor and the computer readable storage medium for realizing workpiece positioning in the numerical control large glass edge grinding process, the bottleneck of traditional mechanical positioning is broken through, high-precision original point calibration and angle rotation control are realized through an intelligent visual system, and the purpose of safely, accurately and efficiently finishing edge processing of large-size glass is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of glass, rock plate, stone material, in particular to the field of large glass edge grinding system, and more particularly to a method, system and device for realizing workpiece positioning in a numerical control large glass edge grinding process, a processor and a computer readable storage medium thereof. BACKGROUND

[0002] In a traditional large glass edge grinding system, the shooting position is usually fixed. The disadvantage is that the workpiece must be precisely positioned to the fixed position (by clamps, positioning pins, etc.), otherwise the camera may not be able to shoot, shoot completely or shoot crooked. This requires high precision and consistency of mechanical transmission, and cannot handle cases where the incoming position is deviated.

[0003] In the field of numerical control machining, workpiece positioning is a key link to ensure machining precision and quality, and is related to the success or failure of the entire machining process. The traditional numerical control machining workpiece positioning method mainly relies on mechanical clamps and manual operation, which meets the production needs in a certain period of time, but as manufacturing moves towards high precision, high efficiency and intelligentization, its limitations become increasingly prominent. Low efficiency is a major pain point of traditional positioning methods.

[0004] The manual positioning and checking process is tedious and time-consuming, and workers need to rely on experience and the naked eye to repeatedly adjust the workpiece position and measure and check. When machining complex parts, this process may take a lot of time, seriously affecting production efficiency. Long-term manual operation also easily causes workers to become tired, further reducing work efficiency and causing production progress to be delayed.

[0005] The precision of the traditional positioning method is also extremely limited. The accuracy of the human eye is difficult to match the high-precision machining requirements, and even experienced workers are not up to the task when faced with micron or even nanometer level precision requirements. Mechanical clamps themselves have manufacturing errors and wear problems, and their positioning accuracy will gradually decrease after long-term use, making it difficult to ensure the machining precision of the workpiece.

[0006] The traditional positioning method lacks flexibility. When the shape and size of the workpiece change, the clamps and parameters need to be frequently adjusted. SUMMARY

[0007] The purpose of the present application is to overcome the above-mentioned shortcomings of the prior art, and to provide a method, system, device, processor and computer readable storage medium for realizing workpiece positioning in a numerical control large glass edge grinding process, which meets high flexibility, high machining precision and a relatively wide range of applications.

[0008] In order to achieve the above-mentioned purpose, the method, system, device, processor and computer readable storage medium for realizing workpiece positioning in a numerical control large glass edge grinding process of the present application are as follows: The method for realizing workpiece positioning in the numerical control large glass edging process, which mainly comprises the following steps: (1) workpiece clamping, loading processing drawing; (2) automatically calculating the shooting point; (3) collecting images through the camera and performing pretreatment operation on the images; (4) performing camera calibration to obtain the internal and external parameters of the camera and the distortion parameters; (5) converting the image coordinate system and the machine tool coordinate system.

[0009] Preferably, the step (2) specifically comprises the following steps: (2.1) pre-processing data, analyzing entities and paths; (2.2) performing feature point detection to extract endpoints, centers and vertices; (2.3) performing feature point optimization to remove duplicate points, perform distance filtering and sorting; (2.4) performing feature analysis; (2.5) outputting results.

[0010] Preferably, the step (3) specifically comprises the following steps: (3.1) performing grayscale conversion to convert color images into grayscale images; (3.2) performing binary conversion to convert grayscale images into black and white images; (3.3) selecting a suitable edge detection algorithm for edge detection according to the characteristics of the images and detection requirements.

[0011] Preferably, the step (3.2) specifically comprises the following steps: A threshold value is set, if the pixel gray value is higher than the threshold value, the pixel gray value in the image is set to white; if the pixel gray value is lower than the threshold value, the pixel gray value in the image is set to black.

[0012] Preferably, the step (3.3) specifically comprises the following steps: (3.3.1) performing contour detection; (3.3.2) filtering the contour; (3.3.3) analyzing the contour; (3.3.4) extracting feature points; (3.3.5) deriving the contour point set.

[0013] Preferably, the step (4) specifically comprises the following steps: (4.1) using a chessboard calibration board, by shooting different angle calibration board image, using image detection algorithm to get each corner pixel coordinates; (4.2) according to the size of the grid on the calibration board, calculate each corner in the world coordinate system under the physical coordinates; (4.3) using these pixel coordinates and world coordinate information of the corner, camera calibration, solve the camera internal and external parameter matrix and distortion parameters.

[0014] Preferably, the step (5) is specifically: establish the conversion relationship between the image coordinate system and the machine tool coordinate system, and convert the detected workpiece position information in the image into the coordinates in the machine tool coordinate system.

[0015] The system for realizing workpiece positioning in the process of numerical control large glass edging, its main feature is, the system includes camera, image acquisition card and lighting equipment, the camera is connected with the image acquisition card, the image acquisition card is installed on the computer, and is connected with the computer, the computer is connected with the machine tool;The camera is used for collecting workpiece image, the image acquisition card is used for transmitting the image signal collected by the camera to the computer, and the lighting equipment is used for providing different types of lighting equipment for different workpiece detection scenes.

[0016] The device for realizing workpiece positioning in the process of numerical control large glass edging, its main feature is, the device includes: processor, configured to execute computer executable instructions; memory, storing one or more computer executable instructions, the computer executable instructions are executed by the processor, realize each step of the above-mentioned method for realizing workpiece positioning in the process of numerical control large glass edging.

[0017] The processor for realizing workpiece positioning in the process of numerical control large glass edging, its main feature is, the processor is configured to execute computer executable instructions, the computer executable instructions are executed by the processor, realize each step of the above-mentioned method for realizing workpiece positioning in the process of numerical control large glass edging.

[0018] The computer readable storage medium, its main feature is, it is stored with computer program, the computer program can be executed by the processor to realize each step of the above-mentioned method for realizing workpiece positioning in the process of numerical control large glass edging.

[0019] The method, system, device, processor and computer readable storage medium for realizing workpiece positioning in the numerical control large glass edge grinding process of the application break through the traditional mechanical positioning bottleneck, realize high-precision origin calibration and angle rotation control through an intelligent vision system, and aim to safely, accurately and efficiently complete the edge processing of large-size glass. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The figure is a flowchart of the method for realizing workpiece positioning in the numerical control large glass edge grinding process of the application.

[0021] Figure 2 The figure is a flowchart of the photographing point calculation of the method for realizing workpiece positioning in the numerical control large glass edge grinding process of the application.

[0022] Figure 3 The figure is a flowchart of the contour extraction of the method for realizing workpiece positioning in the numerical control large glass edge grinding process of the application.

[0023] Figure 4 The figure is a flowchart of the workpiece offset calculation of the method for realizing workpiece positioning in the numerical control large glass edge grinding process of the application.

[0024] Figure 5 The figure is a structural diagram of the system for realizing workpiece positioning in the numerical control large glass edge grinding process of the application. DETAILED DESCRIPTION

[0025] In order to more clearly describe the technical content of the application, the following further describes in combination with specific embodiments.

[0026] The method for realizing workpiece positioning in the numerical control large glass edge grinding process of the application includes the following steps: (1) Workpiece clamping, loading processing drawing; (2) Automatically calculating photographing points; (3) Collecting images through a camera, and performing pre-processing operation on the images; (4) Camera calibration, obtaining internal parameters, external parameters and distortion parameters of the camera; (5) Image coordinate system and machine tool coordinate system conversion.

[0027] As a preferred embodiment of the application, the step (2) specifically includes the following steps: (2.1) Data pre-processing, analyzing entities and paths; (2.2) Feature point detection, extracting endpoints, centers and vertices; (2.3) Feature point optimization, removing duplicate points, and performing distance filtering and sorting; (2.4) Feature analysis; (2.5) outputting the result.

[0028] As a preferred embodiment of the present application, the step (3) specifically comprises the following steps: (3.1) performing grayscale processing to convert the color image into a grayscale image; (3.2) performing binary processing to convert the grayscale image into a black-and-white image; (3.3) selecting a suitable edge detection algorithm according to the characteristics of the image and the detection requirements to perform edge detection.

[0029] As a preferred embodiment of the present application, the step (3.2) specifically comprises the following steps: setting a threshold value, if the grayscale value of a pixel is higher than the threshold value, setting the grayscale value of the pixel in the image to white; if the grayscale value of a pixel is lower than the threshold value, setting the grayscale value of the pixel in the image to black.

[0030] As a preferred embodiment of the present application, the step (3.3) specifically comprises the following steps: (3.3.1) performing contour detection; (3.3.2) filtering the contour; (3.3.3) analyzing the contour; (3.3.4) extracting feature points; (3.3.5) deriving a contour point set.

[0031] As a preferred embodiment of the present application, the step (4) specifically comprises the following steps: (4.1) using a checkerboard calibration plate, by shooting images of the calibration plate at different angles, using image detection algorithms to obtain the pixel coordinates of each corner point; (4.2) according to the size of the grid on the calibration plate, calculating the physical coordinates of each corner point in the world coordinate system; (4.3) using the pixel coordinates and world coordinate information of these corner points, calibrating the camera to solve the internal and external parameter matrices and distortion parameters of the camera.

[0032] As a preferred embodiment of the present application, the step (5) specifically comprises: establishing the conversion relationship between the image coordinate system and the machine tool coordinate system, converting the detected workpiece position information in the image into coordinates in the machine tool coordinate system.

[0033] The system for realizing workpiece positioning in the numerical control large glass edging process of the application, wherein the system comprises a camera, an image acquisition card and an illumination device, the camera is connected with the image acquisition card, the image acquisition card is installed on a computer and connected with the computer, and the computer is connected with a machine tool; the camera is used for collecting workpiece images, the image acquisition card is used for transmitting the image signals collected by the camera to the computer, and the illumination device is used for providing different types of illumination devices for different workpiece detection scenes.

[0034] The device for realizing workpiece positioning in the numerical control large glass edging process of the application, wherein the device comprises: a processor configured to execute computer executable instructions; a memory storing one or more computer executable instructions, which, when executed by the processor, realize the steps of the method for realizing workpiece positioning in the numerical control large glass edging process.

[0035] The processor for realizing workpiece positioning in the numerical control large glass edging process of the application, wherein the processor is configured to execute computer executable instructions, which, when executed by the processor, realize the steps of the method for realizing workpiece positioning in the numerical control large glass edging process.

[0036] The computer readable storage medium of the application, which stores a computer program, the computer program can be executed by a processor to realize the steps of the method for realizing workpiece positioning in the numerical control large glass edging process.

[0037] In the face of various disadvantages of the traditional numerical control workpiece positioning method, it is urgent to seek a more efficient, accurate and flexible positioning scheme. The emergence of visual positioning technology brings new opportunities to solve these problems, and is expected to become the key force to promote the development of numerical control processing industry.

[0038] The application relates to the field of numerical control door large glass edging, and can be applied to the glass, rock plate and stone material industries, and has the same process and high reusability. The method comprises the following steps: acquiring an image through a camera, and calculating the accurate position (X, Y) and rotation angle (theta) of the workpiece in the machine tool coordinate system by using an image processing algorithm.

[0039] I. Visual positioning system composition The visual positioning system is mainly composed of hardware and software, and the hardware part is the "skeleton" and "senses" of the system, including a camera, an image acquisition card, an illumination device and other key components.

[0040] The camera, as the "eyes" of the visual positioning system, undertakes the task of capturing workpiece images, and its performance is directly related to the positioning accuracy.

[0041] An image acquisition card acts as a "bridge" between the camera and the computer, responsible for quickly and accurately transmitting the image signals captured by the camera to the computer. It functions like a high-speed information channel, ensuring efficient transmission of image data and preventing data loss or delays.

[0042] Lighting equipment provides suitable illumination conditions for workpieces, thereby improving image quality and feature recognition. Different types of lighting equipment, such as strip lights, ring lights, and backlights, are suitable for different workpiece inspection scenarios. For metal workpieces with strong surface reflectivity, ring lights can effectively reduce reflections and highlight the edges and surface features of the workpiece; while for transparent or semi-transparent workpieces, backlights can provide clear outlines and images of internal structures. The setup techniques for lighting equipment are also crucial. It is necessary to ensure that it works synchronously with the camera, avoid direct light hitting the camera lens causing light pollution, and reduce shadows and optimize illumination uniformity by adjusting the angle and intensity of the light.

[0043] II. Basic Image Processing Operations After the camera completes image acquisition, the raw image often contains a large amount of redundant information and noise, making it difficult to use directly for workpiece positioning analysis. Therefore, a series of preprocessing operations are required. Basic image processing operations mainly include grayscale conversion, binarization, and edge detection.

[0044] Grayscale conversion is the first step in image processing. It converts a color image into a grayscale image, simplifying the image data and reducing the complexity of subsequent processing. In a color image, each pixel consists of three color channels: red (R), green (G), and blue (B), while a grayscale image contains only one brightness channel.

[0045] Binarization is the process of converting a grayscale image into a black-and-white image, that is, setting the grayscale value of each pixel in the image to 0 (black) or 255 (white), eliminating intermediate grayscale levels. This process is accomplished by setting a threshold; if the grayscale value of a pixel is higher than the threshold, it is set to white; if it is lower than the threshold, it is set to black. Binarization is widely used in image segmentation, edge detection, and other fields, highlighting specific parts of an image and facilitating subsequent feature extraction and analysis.

[0046] Edge detection is a key step in identifying the edges of objects in an image, which usually correspond to areas where the gray level changes sharply, representing the outline of the object. Common edge detection algorithms include Sobel operator, Canny operator, Prewitt operator, etc. Sobel operator is a discrete differential operator that combines Gaussian smoothing and differential derivation. It calculates the convolution results of two convolution kernels with the image by weighted averaging in horizontal and vertical directions, thereby highlighting edge information and achieving good detection results for gradient direction. Canny operator is considered one of the best edge detection operators in terms of performance. It realizes a multi-stage process through optimization of mathematical models, including noise smoothing, edge enhancement, detection, and positioning, which can suppress noise while obtaining accurate edge positions. Prewitt operator is also a differential-based edge detection operator that processes images through two convolution kernels to calculate the gradients in horizontal and vertical directions, which is not sensitive to edge direction and suitable for scenes where the edge direction is unknown. In practical applications, appropriate edge detection algorithms need to be selected based on the characteristics of the image and detection requirements.

[0047] III. Camera Calibration and Coordinate Transformation Camera calibration is a key step in visual positioning technology. It establishes a precise "measuring scale" for the camera, which is used to obtain the intrinsic and extrinsic parameters of the camera, thereby achieving accurate conversion between the image coordinate system and the machine tool coordinate system. The intrinsic parameters of the camera describe the imaging characteristics of the camera itself, such as focal length, principal point coordinates, distortion coefficients, etc. The extrinsic parameters describe the position and orientation of the camera relative to a reference coordinate system (usually the world coordinate system or the machine tool coordinate system).

[0048] In actual imaging processes, due to factors such as lens optical structure, imaging characteristics, processing errors, etc., the camera inevitably produces distortion, including radial distortion, tangential distortion, and in-plane distortion. Radial distortion is caused by lens processing errors, resulting in a radial or positive or negative offset between the actual image point and the theoretical image point. It is characterized by strict symmetry along the lens center, and the size of the image point distortion is related to its distance from the lens center, which is generally divided into barrel distortion and pincushion distortion. Tangential distortion is caused by manufacturing defects of the camera and the non-parallelism between the lens and the imaging plane during installation, which manifests as a change in the vector endpoint along the tangent direction, i.e., a change in angle. In-plane distortion includes distortion caused by non-flat imaging plane and in-plane distortion. To eliminate the impact of these distortions on the image, accurate distortion coefficients need to be obtained through camera calibration, and correction needs to be performed in subsequent image processing.

[0049] The commonly used camera calibration method utilizes a checkerboard calibration board, a series of calibration board images at different angles are shot, pixel coordinates of each corner point are obtained by using image detection algorithm, and meanwhile, the size of the grid on the calibration board is known, so that the physical coordinates of each corner point in the world coordinate system are calculated. Then, the pixel coordinates and the world coordinate information of the corner points are used to calibrate the camera, and the internal and external parameter matrices and the distortion parameters of the camera are solved.

[0050] After the internal and external parameters of the camera are obtained, the conversion between the image coordinate system and the machine tool coordinate system is needed. The image coordinate system is a coordinate system for describing the position of a pixel point in an image, and the origin thereof is usually located at the upper left corner of the image. The machine tool coordinate system is a coordinate system inherent to a numerical control machine tool, and is used to determine the position and motion trajectory of a workpiece on the machine tool. In order to realize accurate positioning of the workpiece by the vision positioning system, the conversion relationship between the image coordinate system and the machine tool coordinate system must be established, so as to convert the position information of the workpiece detected in the image into coordinates in the machine tool coordinate system, thereby controlling the machine tool to accurately machine the workpiece.

[0051] The specific implementation scheme of the embodiment can be referred to the related description in the above embodiment, which will not be repeated here.

[0052] It can be understood that the same or similar parts in the above embodiments can be mutually referred to, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0053] It should be noted that, in the description of the present application, the terms "first", "second", etc. are only used for the purpose of description, and cannot be understood as indicating or implying relative importance. In addition, in the description of the present application, unless otherwise specified, the meaning of "a plurality of" is at least two.

[0054] Any process or method descriptions in flow charts or otherwise described herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions or other processes, and the various embodiments of the present application include additional implementations in which the functions described in the illustrated or other described processes are performed in different orders, including substantially simultaneously, or in reverse order, or in other orders, depending on the functionality involved, as will be understood by those skilled in the art.

[0055] It should be understood that each part of the present application can be realized by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if realized by hardware, and as in another embodiment, it can be realized by any one or a combination of the following technologies known in the art: discrete logic circuit with logic gate circuit for implementing logic function on data signal, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) and the like.

[0056] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment method can be completed by a program instructing the relevant hardware, and the corresponding program can be stored in a computer readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0057] In addition, each functional unit in each embodiment of the present application can be integrated in one processing module, or each unit can exist physically separately, or two or more units can be integrated in one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of software functional module. The integrated module, if realized in the form of software functional module and sold or used as an independent product, can also be stored in a computer readable storage medium.

[0058] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0059] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" 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 application. In the present specification, the illustrative description 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.

[0060] The method, system, device, processor and computer readable storage medium thereof for realizing workpiece positioning in the numerical control large glass edge grinding process of the present application break through the bottleneck of traditional mechanical positioning, realize high-precision origin calibration and angle rotation control through intelligent vision system, and aim to safely, accurately and efficiently complete the edge processing of large-size glass.

[0061] In this specification, the application has been described with reference to specific embodiments thereof. It is apparent, however, that various modifications and changes can be made thereto without departing from the spirit and scope of the application. Therefore, the description and drawings should be regarded in an illustrative rather than a restrictive sense.

Claims

1. A method for workpiece positioning in a numerically controlled large glass edging process, characterized in that, The method comprises the following steps: (1) workpiece clamping, loading processing drawing; (2) automatically calculating the shooting point; (3) acquiring image through camera, and performing pretreatment operation on the image; (4) performing camera calibration, obtaining internal parameters and external parameters of the camera, and distortion parameters; (5) converting the image coordinate system and the machine tool coordinate system.

2. The method of claim 1, wherein, The step (2) specifically comprises the following steps: (2.1) data preprocessing, analyzing entities and paths; (2.2) feature point detection, extracting endpoints, circle centers and vertexes; (2.3) feature point optimization, removing duplicate points, and performing distance filtering and sorting; (2.4) feature analysis; (2.5) outputting results.

3. The method of claim 1, wherein, The step (3) specifically comprises the following steps: (3.1) performing grayscale, converting color image into grayscale image; (3.2) performing binaryzation, converting grayscale image into black and white image; (3.3) selecting appropriate edge detection algorithm for edge detection according to the characteristics of the image and detection requirements.

4. The method of claim 3, wherein, The step (3.2) specifically comprises the following steps: Setting a threshold, if the gray value of the pixel is higher than the threshold, the gray value of the pixel in the image is set to white; if the gray value of the pixel is lower than the threshold, the gray value of the pixel in the image is set to black.

5. The method of claim 3, wherein, The step (3.3) specifically comprises the following steps: (3.3.1) performing contour detection; (3.3.2) filtering the contour; (3.3.3) analyzing the contour; (3.3.4) extracting feature points; (3.3.5) exporting contour point set.

6. The method of claim 1, wherein, The step (4) specifically comprises the following steps: (4.1) using a checkerboard calibration board, shooting images of the calibration board at different angles, and using image detection algorithm to obtain pixel coordinates of each corner point; (4.2) calculating physical coordinates of each corner point in the world coordinate system according to the size of the grid on the calibration board; (4.3) using the pixel coordinates and world coordinate information of these corner points, calibrating the camera, and solving the internal and external parameter matrices and distortion parameters of the camera.

7. The method of claim 1, wherein, The step (5) is specifically: Establishing conversion relationship between the image coordinate system and the machine tool coordinate system, and converting the detected workpiece position information in the image into coordinates in the machine tool coordinate system.

8. A system for achieving workpiece positioning during numerically controlled large glass edging, characterized by, The system comprises a camera, an image acquisition card and an illumination device, the camera is connected with the image acquisition card, the image acquisition card is installed on a computer and connected with the computer, and the computer is connected with a machine tool; the camera is used for acquiring workpiece images, the image acquisition card is used for transmitting image signals collected by the camera to the computer, and the illumination device is used for providing different types of illumination devices for different workpiece detection scenes.

9. A device for positioning a workpiece during numerically controlled edging of a large glass sheet using the method of claim 1, wherein, The device comprises: a processor configured to execute computer executable instructions; a memory storing one or more computer executable instructions, which, when executed by the processor, implement each step of the method for positioning workpiece in the process of numerical control large glass edging according to any one of claims 1 to 7.

10. A processor for effecting workpiece positioning during numerically controlled large glass edging, the processor comprising: The processor is configured to execute computer executable instructions which, when executed by the processor, implement the steps of the method for workpiece positioning in a numerically controlled large glass edging process according to any one of claims 1 to 7.

11. A computer readable storage medium characterized by, A computer program is stored on the computer readable medium, the computer program being executable by a processor to implement the steps of the method for workpiece positioning in a numerically controlled large glass edging process according to any one of claims 1 to 7.

Images