Process detection method, device, equipment and product
Through each processing link of the coil winding process, point cloud data is obtained and image depth processing is performed, the problems of poor manual detection accuracy and high cost are solved, and automated detection and high-precision dimensional measurement are realized.
Patent Information
- Application Number
- CN202411820050.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-11
- Publication Date
- 2025-03-21
AI Technical Summary
In the prior art, the detection of the coil winding process relies on labor, and there are problems of high cost, poor accuracy and poor sustainability, making it difficult to achieve high-efficiency and accurate detection.
A process detection method is proposed, by obtaining point cloud data of the workpiece to be tested at each processing link, establishing a depth map, and obtaining the attribute information of the workpiece through the image depth processing method, and detecting according to the process standards.
Automatic defect detection of coil products and tolerance-based dimensional measurement judgment are realized, which reduces manual inspection costs, improves product inspection quality, and improves detection accuracy.
Smart Images
Figure CN119666071A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of transformer production, and in particular to a process detection method, device, equipment and product. Background Art
[0002] The coil is the core of the transformer's transformation and transmission and distribution of electrical energy. The process size of the coil winding process is an important factor affecting the performance and stability of the transformer. In the relevant technology, due to the complex coil winding process, the coil winding and its detection of ultra-high voltage transformers are currently mainly completed manually.
[0003] However, manual detection has defects such as high cost, poor accuracy and poor sustainability.
[0004] Therefore, how to accurately detect the coil winding process more efficiently is a problem that needs to be solved urgently. Summary of the invention
[0005] The main purpose of this application is to provide a process detection method, device, equipment and product, aiming to solve the technical problem of how to accurately detect the coil winding process more efficiently.
[0006] To achieve the above purpose, the present application proposes a process detection method, which includes: After binding with the workpiece to be tested, obtaining the process standard corresponding to the workpiece to be tested in each processing link; For each processing link, obtaining point cloud data of the workpiece to be measured in the processing link in a corresponding state; Establishing a depth map of the workpiece to be measured in the processing link according to the point cloud data; Processing the depth map by an image depth processing method to obtain attribute information of the workpiece to be measured in the state; According to the attribute information and the process standard, the detection result of the workpiece to be detected in each of the processing links is obtained.
[0007] In some embodiments, the processing link includes a mold detection link, and the workpiece to be detected in the mold detection link is a winding mold; the step of obtaining point cloud data of the workpiece to be detected in the processing link in a corresponding state includes: According to the mold detection parameters, the laser profiler is controlled to scan the three areas of the winding mold respectively to obtain point cloud data of the winding mold; The step of processing the depth map by an image depth processing method to obtain the property information of the workpiece to be measured in the state includes: Using dynamic threshold segmentation to extract target parts of the three regions in the depth map; Based on the target portion, the verticality of the winding mold and the cross-sectional perimeter deviation values between different target portions are calculated.
[0008] In some embodiments, the processing link includes an insulating cylinder detection link, and the workpiece to be detected in the insulating cylinder detection link is an insulating cylinder; the step of obtaining point cloud data of the workpiece to be detected in the processing link in a corresponding state includes: According to the detection parameters of the insulating cylinder, the laser profiler is controlled to scan the three areas of the insulating cylinder respectively to obtain point cloud data of the insulating cylinder; The step of processing the depth map by using an image depth processing method to obtain the property information of the workpiece to be measured in the state includes: Using a traversal area algorithm and / or a dynamic threshold segmentation algorithm, extracting bottom end surface information, a strut area, and an insulating tube area in the depth map of the insulating tube; Based on the bottom end face information, the strut area and the insulating cylinder area, the insulating cylinder verticality, the strut verticality, the insulating cylinder circumference deviation, the strut spacing, the insulating cylinder outer diameter and the bottom end face deviation size in the insulating cylinder detection link are calculated.
[0009] In some embodiments, the processing link includes a winding link, and the workpiece to be measured in the winding link is a coil being wound; and obtaining point cloud data of the workpiece to be measured in the processing link in a corresponding state includes: During the winding process, the number of winding turns is obtained in real time; Generating a coil winding model composed of point cloud data in real time according to the number of winding turns; The step of establishing a depth map of the workpiece to be measured in the processing link according to the point cloud data includes: Generating a depth map corresponding to the number of winding turns according to the coil winding model; The step of processing the depth map by using an image depth processing method to obtain the property information of the workpiece to be measured in the state includes: Determining a winding direction of the coil based on maximum depth values of different areas in the depth map and a preset depth threshold; Using a traversal area algorithm and / or a dynamic threshold segmentation algorithm, extracting a coil area and a strut area in the depth map of the number of winding turns; Performing filtering and downsampling on the coil region and the strut region to obtain a data fitting surface; The surface is fitted based on the data to calculate the coil outer diameter, coil inner diameter, coil radial size, axial protrusion, line segment unevenness and strut spacing.
[0010] In some embodiments, the processing link includes a post-winding link, and the workpiece to be measured in the winding link is a coil that has been wound; and obtaining point cloud data of the workpiece to be measured in the processing link in a corresponding state includes: According to the post-winding detection parameters, the laser profiler is controlled to scan three areas of the wound coil respectively to obtain point cloud data of the wound coil; The step of processing the depth map by an image depth processing method to obtain the property information of the workpiece to be measured in the state includes: A global threshold segmentation algorithm is used to extract a coil area and a pad area from the depth map of the wound coil; Using a dynamic threshold segmentation algorithm and a morphological algorithm, extracting an upper edge line and a lower edge line in the depth map of the insulating cylinder; Based on the upper edge line and the lower edge line, obtaining the height of the coil; Based on the grayscale histogram distribution, the center points of the pad area are connected, and the verticality size of the pad is obtained according to the ratio of the horizontal coordinate deviation and the vertical coordinate deviation of the connecting line.
[0011] In some embodiments, after obtaining the detection result of the workpiece to be tested in each processing link according to the attribute information and the process standard, the method further includes: In any processing link, if the test result does not meet the corresponding process standard in the processing link, an alarm signal is issued to indicate process abnormality; The acquired attribute information is sent to the server for storage.
[0012] In some embodiments, for each processing link, obtaining point cloud data of the workpiece to be measured in the processing link in a corresponding state includes: In response to receiving the calibration instruction, controlling the laser profiler to move so that the laser calibration line follows the movement; When the laser calibration line is detected to move to a specified position, the laser profiler is controlled to stop moving to complete the calibration; When the laser profiler is calibrated, for each processing link, point cloud data of the workpiece to be measured in the corresponding state in the processing link is obtained.
[0013] In addition, to achieve the above purpose, the present application also proposes a process detection device, which includes: A standard acquisition module, used to acquire the process standard corresponding to the workpiece to be tested in each processing link after being bound to the workpiece to be tested; A point cloud acquisition module, used for acquiring point cloud data of the workpiece to be measured in a corresponding state in each processing link; An image creation module, used to create a depth map of the workpiece to be measured in the processing link according to the point cloud data; A data analysis module, used for processing the depth map by an image depth processing method to obtain attribute information of the workpiece to be measured in the state; The property detection module is used to obtain the detection result of the workpiece to be detected in each processing link according to the property information and the process standard.
[0014] In addition, to achieve the above objectives, the present application also proposes a process detection device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the process detection method as described above.
[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the process detection method as described above are implemented.
[0016] One or more technical solutions proposed in this application have at least the following technical effects: After binding with the workpiece to be tested, the corresponding process standards of the workpiece to be tested in each processing link are obtained, and the attribute information is automatically obtained for comparison in each subsequent link, thereby realizing the automatic defect detection of coil products and the dimension measurement judgment based on tolerance, reducing the cost of manual inspection and improving the quality of product inspection. In addition, this embodiment processes the point cloud data and the depth map, calculates the various attribute information of the workpiece by data fitting, image depth processing methods, etc., and can accurately calculate the various attribute information of the workpiece with a more unified standard, which is more accurate than manual measurement. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0019] Figure 1 A schematic diagram of a process detection method provided in an embodiment of the present application is shown; Figure 2 A schematic diagram of the structure of a process detection device provided in an embodiment of the present application is shown; Figure 3 A schematic structural diagram of a process detection device provided in an embodiment of the present application is shown.
[0020] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION
[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.
[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.
[0023] The main solution of the embodiment of the present application is: after binding with the workpiece to be tested, obtaining the corresponding process standards of the workpiece to be tested in each processing link; for each processing link, obtaining the point cloud data of the workpiece to be tested in the corresponding state in the processing link; using the point cloud data, obtaining the attribute information of the workpiece to be tested in the state; according to the attribute information and the process standards, obtaining the detection results of the workpiece to be tested in each of the processing links.
[0024] In the relevant technology, the coil is the core of the transformer's transformation and transmission and distribution of electric energy. The process size during the coil winding process is an important factor affecting the performance and stability of the transformer. Due to the complex process, the coil winding and detection of ultra-high voltage transformers are mainly completed manually. However, manual detection has the following problems: 1. Low precision: Due to human factors and limitations of measuring tools, it is difficult to ensure that the size of each coil meets the standard requirements, resulting in inconsistent sizes of manufactured coils, affecting the overall performance and stability of the transformer.
[0025] 2. Low detection efficiency: Since the coil size is complex and varied, operators need to repeatedly measure and adjust, which consumes a lot of time and human resources, and has problems such as inconvenient operation and slow measurement speed, which seriously affects production efficiency and delivery cycle.
[0026] 3. Poor sustainability: Due to reliance on manual measurement, the coil winding process is unsustainable, with large quality fluctuations and long production cycles. The operator's technical level, working conditions and other factors will affect the quality and production efficiency of the coil, resulting in high uncertainty and risk in the winding process, which is not conducive to stable production and quality controllability.
[0027] In summary, how to accurately detect the coil winding process more efficiently is a problem that needs to be solved urgently.
[0028] Based on this, the present application provides a solution that can accurately detect the coil winding process more efficiently than related technologies.
[0029] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a process detection device capable of realizing the above functions, etc. The following takes the process detection device as an example to illustrate this embodiment and the following embodiments.
[0030] To facilitate understanding, some terms related to transformer coil winding that appear in subsequent process inspection methods are explained here.
[0031] Transformer: A transformer is a device that uses the principle of electromagnetic induction to change AC voltage. It is mainly composed of a primary coil, a secondary coil, and an iron core (magnetic core). When AC is applied to the primary side, a magnetic flux is generated in the coil, which passes through the iron core and induces an electromotive force on the secondary side. According to the ratio of the coil, the voltage and current can be adjusted to the required level on the secondary side, thereby achieving voltage transformation.
[0032] Coil: The coil in a transformer is a winding made of conductive material (such as copper or aluminum) wound around an iron core. Transformers usually have two or more coils, one of which is called the "primary" or "input coil" and the other is called the "secondary" or "output coil."
[0033] Mold: The coil winding mold of the transformer is a tool used to wind the transformer coil. It usually consists of a winding mold and a demoulding device. Through the mold, the coil can be wound according to the specified size and shape, thereby ensuring that the coil winding meets specific requirements.
[0034] Insulation cylinder: Transformer insulation cylinder is a device used to isolate the internal components and outer casing of the transformer. Its main function is to protect the internal components of the transformer from the influence of the external environment and prevent faults such as short circuits between the internal components and the outer casing.
[0035] Stay: The stay connects the coil and the core to provide good support and fixation for the coil, thus preventing the coil from being displaced or deformed due to external forces.
[0036] The relationship between the insulating cylinder, coil and support bar: The insulating cylinder of the transformer is located outside the coil and plays the role of electrical insulation. The height of the insulating cylinder is adapted to the height of the coil. A plurality of support bars are arranged between the insulating cylinder and the outer wall of the coil. All the support bars are arranged at intervals along the outer wall of the coil. Multiple turns of insulating tape are wound around the outer circumference of the support bars. The multiple turns of insulating tape are distributed up and down along the support bars to form the insulating cylinder.
[0037] Reference Figure 1 , Figure 1 The process flow diagram of the process detection method provided by an embodiment of the present application is shown. The process detection method can be applied to process detection equipment, including the following steps S110 to S130: Step S110, after being bound to the workpiece to be tested, obtaining the process standards corresponding to the workpiece to be tested in each processing link.
[0038] The processing links refer to several key processes of coil winding, which may include the pre-winding link, the winding link and the post-winding link in chronological order. The pre-winding link, the winding link and the post-winding link may be further subdivided into smaller processing links, please refer to the relevant parts in the following embodiments.
[0039] The workpiece to be tested refers to the workpiece waiting for coil winding to be executed. The state of the workpiece to be tested is different in different processing links. For example, before winding begins, the workpiece to be tested only includes a corresponding mold; as the winding proceeds, the coil is gradually wound around the iron core through the mold, and finally a complete workpiece with winding is obtained.
[0040] In some embodiments, the process detection equipment may include a mobile device and a terminal device. The staff may scan the identification code on the coil through the mobile device to obtain the model of the coil and upload it to the terminal device. The terminal device determines the corresponding mold model, work order number, process standard, etc. according to the model of the coil. In some feasible embodiments, the mobile device may be a handheld barcode scanner, and the terminal device may be a server, which is not limited in the embodiments of the present application.
[0041] The process standard refers to the pre-set standard value for matching and comparison. Meeting the standard value means that the corresponding workpiece to be tested meets the basic requirements of the current processing link. It should be noted that the workpieces to be tested in different processing links are different, and the items to be tested are also different, so the process standards are also different.
[0042] Step S120, for each processing link, obtaining the point cloud data of the workpiece to be tested in the corresponding state in the processing link.
[0043] Point cloud data refers to a collection of discrete points in three-dimensional space. Each point contains coordinate information (X, Y, Z) in a three-dimensional coordinate system. Therefore, the geometric shape of the object surface can be accurately described based on point cloud data.
[0044] In the embodiment of the present application, point cloud data can be obtained by scanning the workpiece to be measured by a 3D laser profiler. Specifically, when scanning the workpiece to be measured, the workpiece to be measured can be controlled to perform a 360° rotation, so that the 3D laser profiler can completely scan the surface of the workpiece to be measured, and obtain the three-dimensional coordinates, reflectivity, texture and other information of a large number of dense points on the surface of the workpiece to be measured, so as to quickly establish a three-dimensional model of the workpiece to be measured.
[0045] Step S130: establishing a depth map of the workpiece to be measured in the corresponding processing link according to the point cloud data.
[0046] Step S140, processing the depth map by an image depth processing method to obtain attribute information of the workpiece to be measured in a corresponding state.
[0047] In some implementations, in order to measure the size of the workpiece to be measured, the point cloud data may be first converted into a three-dimensional depth map.
[0048] Specifically, converting point cloud data into a three-dimensional depth map may include the following steps: ① Preprocessing of point cloud data Point cloud data may contain noise, missing data, and discontinuity problems, so the point cloud data can be preprocessed, such as denoising, hole filling, surface reconstruction, etc., to obtain more usable point cloud data.
[0049] ② Projection of point cloud data Projecting point cloud data onto a two-dimensional plane is usually achieved using sensor models and coordinate transformations. This step generally requires combining the sensor's pre-calibrated internal and external parameters with rotation matrices, translation vectors, etc., to convert the three-dimensional coordinates of each point in the point cloud data into the coordinates of the point in the world coordinate system.
[0050] ③ Generation of depth map Based on the projected point cloud data, the parameters of the sensor are usually used to calculate and generate a depth map, where the pixel value and depth value of the depth map represent the distance from the point to the sensor.
[0051] After obtaining the depth map based on the above steps, calculations can be performed on the content that needs to be detected in the current processing link.
[0052] For ease of understanding, the following will further explain step S120 to step S140 in chronological order for the entire coil winding process. The coil winding process includes mold detection, insulation tube detection, winding, and post-winding in chronological order.
[0053] (1) Mold inspection In some implementations, the laser profiler may be controlled to scan three areas of the winding mold respectively according to the mold detection parameters to obtain point cloud data of the winding mold.
[0054] Among them, the mold detection parameters refer to pre-set control parameters applied to the aforementioned 3D laser profiler. Through the mold detection parameters, the laser profiler can be controlled to scan a specified area, thereby obtaining point cloud data of the specified area.
[0055] Since the size of the mold is not uniform, it may affect the size of the subsequent coil winding, so it is necessary to detect the size of the mold at different positions. To this end, in this embodiment, the designated area is set as three areas, namely, the upper, middle and lower parts of the winding mold. Among them, the boundaries of the upper, middle and lower parts can be set by the staff according to the specific situation of the mold, and this embodiment does not limit it here.
[0056] After combining the point cloud data of the three parts, the depth map of the entire winding mold can be calculated through the overall point cloud data.
[0057] In this embodiment, dynamic threshold segmentation may be used to extract a target portion from the depth map, wherein the target portion refers to a portion in the winding mold.
[0058] Specifically, in dynamic threshold segmentation, the dynamic threshold can be obtained by the following expression:
[0059] Where T(x,y) is the local threshold at position (x,y); I(x,y) is the pixel value of the image at position (x,y); N is the total number of pixels in the window, and a and b are the radii of the window in the x and y directions, respectively.
[0060] After obtaining the target area, the target area is divided into a plurality of sub-areas arranged in sequence from top to bottom according to a preset rule, and the divided sub-areas are traversed one by one from top to bottom, and the central horizontal coordinate value and the true depth value of the sub-area are calculated one by one.
[0061] Among them, each central horizontal coordinate value corresponds to a preset encoder angle value. According to the preset encoder angle value and the true depth value, the circular cross-section of the mold can be fitted. The data obtained from the above three scans correspond to the three circular cross-sections of the mold respectively. By calculating the cross-sectional circumferences of the three circles, the deviation values of the cross-sectional circumferences of the three parts of the mold can be obtained.
[0062] Among them, the mathematical expression of the sum of squares of the circular fitting residuals is:
[0063] Where (a, b) is the coordinates of the center of the circle and r is the radius of the circle.
[0064] According to the least squares principle, we have:
[0065] From this, we can derive the specific values of the a, b, and r parameters.
[0066] Based on the above expression, the comparison result between the final calculated deviation value and the reference value in the process standard can be used as one of the test results in the mold inspection link.
[0067] In addition, the ratio of the vertical coordinate value to the depth value of the top and bottom of the winding mold can be calculated through the point cloud data of the entire winding mold, so as to obtain the verticality of the entire winding mold.
[0068] (2) Insulation tube detection link In this embodiment, the detection of the insulating tube includes the following six detection items: insulating tube verticality, support bar verticality, insulating tube circumference deviation, support bar spacing, insulating tube outer diameter and bottom end surface deviation size.
[0069] Specifically, in this embodiment, the corresponding insulation cylinder detection parameters can also be pre-set to control the laser profiler to scan the three areas (upper, middle, and lower) of the insulation cylinder respectively to obtain the point cloud data of the three areas. As mentioned above, the depth map corresponding to the insulation cylinder is obtained based on the point cloud data, and then the above detection items are calculated one by one based on the depth map.
[0070] In some embodiments, the depth map corresponding to the point cloud data of the lower region contains information about the bottom surface of the insulating cylinder, and the bottom surface step position can be extracted by a convenient region algorithm, thereby segmenting the portion in the insulating cylinder.
[0071] The dynamic threshold segmentation algorithm can be used to process the portion of the depth map in the insulating cylinder to extract the strut area and the insulating cylinder area. In some embodiments, based on the strut area and the insulating cylinder area, the ratio of the grayscale difference in the depth direction to the coordinate difference in the vertical direction can be traversed and calculated to obtain the verticality of the insulating cylinder and the verticality of the strut.
[0072] The bottom end face region can be obtained by using dynamic threshold segmentation. In some embodiments, the bottom end face deviation size can be obtained by extracting the upper edge portion of the bottom end face region, performing straight line fitting, and calculating the difference between the vertical coordinates of the two end points of the straight line.
[0073] By adopting an edge extraction algorithm, the left edge of the target strut in the strut area and the left edge of the strut adjacent to the target strut are extracted, so as to measure the distance between the two left edges and obtain the strut spacing.
[0074] In addition, based on the same method as in the above (1), the circumference deviation of the cross section of the insulating tube can also be calculated in this embodiment, which will not be described in detail in this embodiment.
[0075] (3) Winding process In this embodiment, the inspection of the winding process includes the following seven inspection items: coil outer diameter, coil inner diameter, coil radial size, axial protrusion, line segment unevenness and support bar spacing.
[0076] In some embodiments, during the winding process, the number of winding turns can be obtained in real time through a programmable logic controller (PLC); it can be understood that the total number of winding turns of the coil is a pre-set fixed value, so according to the current number of winding turns and the total number of winding turns, a coil winding model composed of point cloud data can be generated in real time according to proportion, and based on the coil winding model, a depth map corresponding to the number of winding turns is generated, and this embodiment will not be repeated here.
[0077] In some embodiments, the winding direction of the coil can be determined based on the maximum depth value of different areas in the depth map and a pre-set depth threshold. Specifically, because the reverse winding image of the coil has greater interference and is in poor condition during winding, and the forward winding image has better quality, the winding direction of the coil needs to be distinguished before detection. The sampling depth map is traversed, the depth map is split into multiple sampling areas in a pre-set segmentation method, and the maximum depth value of each sampling area is calculated. In this embodiment, a depth threshold can be set in advance, and the winding direction of the coil can be determined by the depth threshold. For example, if the maximum depth value exceeds the depth threshold, the coil is considered to be forwardly wound; if the maximum depth value does not exceed the depth threshold, the coil is considered to be reversely wound.
[0078] In addition, in some implementations, if the coil is wound in the forward direction, two sections are detected at one time; if it is wound in the reverse direction, no processing is performed; the following description will be continued by taking the coil being wound in the forward direction as an example.
[0079] In some implementations, the step edge can be extracted by traversing the region algorithm, and the upper half region can be segmented by dynamic threshold value to segment the strut region.
[0080] After obtaining the strut area, the main strut and the secondary strut can be determined according to the depth value of the area below the strut, the right edge line of the secondary strut can be extracted, and the intersection of the right edge line and the step edge can be found. Then, a rectangular area is selected downward according to the intersection position. The rectangular area contains holes and coils, and the holes are filtered in the rectangular area using a dynamic threshold to obtain the coil area.
[0081] In some implementations, since the surfaces of the struts and coils are uneven, the directly extracted regional data is relatively messy, and the point cloud data of the strut regions and the coil regions are filtered and downsampled to reduce the point cloud density and screen the data to fit the surface.
[0082] In some embodiments, the lateral coordinate values and depth values of the strut and coil fitting surfaces can be calculated respectively, and each lateral coordinate value corresponds to a preset angle value. The strut circle and the coil circle (the same as the cross-sectional circle in the above-mentioned related part) can be fitted according to the corresponding angle value and the true depth value, and the strut outer diameter and the coil outer diameter size can be obtained. The difference between the strut outer diameter and the thickness of the inner diameter pad is the coil inner diameter size, and the difference between the coil outer diameter and the coil inner diameter is the coil radial size. In this embodiment, only the upper two sections of the winding layer can be detected, and the difference in the depth values of the upper two sections can be calculated to obtain the line segment unevenness.
[0083] In this embodiment, the method for calculating the spacing between the struts is the same as that in the aforementioned step (2), and will not be described in detail in this embodiment.
[0084] The axial extension of the transformer refers to the structure in which the branch line of the transformer coil is led axially upward from the outermost layer and the second-to-last layer of the coil. In this embodiment, the number of coil turns of a set number can be calculated, and the lower coil area can be obtained by traversing the depth step once, and the lowermost axial extension area can be obtained by traversing the depth step twice in the coil area. The extension area and the coil area are obtained by dynamic threshold segmentation and morphological processing, and the spacing value between the extension area and the left and right coil areas is calculated to obtain the axial extension deviation size.
[0085] (4) Post-winding stage In this embodiment, the detection of the post-winding process includes the following two detection items: coil height and pad verticality.
[0086] Specifically, as mentioned above, the post-winding detection parameters are used to control the laser profiler to scan three areas of the wound coil respectively to obtain point cloud data of the wound coil, and a depth map of the wound coil is generated based on the point cloud data.
[0087] In some embodiments, the depth map corresponding to the upper part can be globally thresholded to obtain the coil area, and the upper edge line can be obtained by dynamic threshold and morphological algorithm processing; similarly, the depth map corresponding to the lower part is processed to obtain the lower edge line, and the coil height size can be calculated according to the coordinate position of the point on the edge line.
[0088] Specifically, the threshold segmentation mathematical expression is:
[0089] Where C represents the pixel value of the binarized image, G represents the pixel value of the original image, T represents the threshold, and i represents the pixel number in the image.
[0090] Specifically, the mathematical expression of the morphological operation to filter out noise points is:
[0091]
[0092] Where A is the input image, B is the structural element, and x and y are the image pixel domains.
[0093] In some embodiments, the pad area can be segmented using grayscale histogram distribution, the center points of the pad area can be connected for straight line fitting, and the ratio of the horizontal coordinate deviation to the vertical coordinate deviation of the straight line can be calculated to obtain the verticality dimension of the pad.
[0094] Step S140, obtaining the detection result of the workpiece to be detected in each processing link according to the attribute information and the process standard.
[0095] Based on the above (1)-(4), the corresponding test results can be obtained for each processing link in the entire winding process. By comparing the test results with the reference values in the corresponding process standards, it can be known whether errors occur in the corresponding processing links, which facilitates the subsequent analysis and search for the causes of the errors.
[0096] In some implementations, in any processing link, if an error occurs and the size of the error reaches a preset alarm condition, the warning information can be immediately visualized and presented to the staff, thereby facilitating the staff to make timely judgments on the content of the error.
[0097] In some embodiments, in any processing link, all acquired data (including point cloud data, depth map, various parameters, detection results, etc.) can be sent to the server in real time, so that when analyzing the cause of the error later, the situation on the scene can be clearly traced back.
[0098] It can be understood that compared with the related art, this embodiment removes most of the irrelevant point cloud data by performing operations such as extraction and denoising of specific areas in the depth map, which reduces the amount of calculation and is more efficient.
[0099] This embodiment provides a process detection method, which obtains the process standard corresponding to the workpiece to be tested in each processing link after binding with the workpiece to be tested, and automatically obtains attribute information for comparison in each subsequent link, thereby realizing automatic defect detection of coil products and tolerance-based dimensional measurement judgment, reducing manual inspection costs, and improving product inspection quality. In addition, this embodiment processes point cloud data and calculates various attribute information of the workpiece by data fitting and other methods, so as to accurately calculate various attribute information of the workpiece with a more unified standard, which is more accurate than manual measurement.
[0100] This application also provides a process detection device, please refer to Figure 2 , the process detection device 100 comprises: The standard acquisition module 110 is used to acquire the process standard corresponding to the workpiece to be tested in each processing link after being bound to the workpiece to be tested; The point cloud acquisition module 120 is used to acquire, for each processing link, point cloud data of the workpiece to be measured in the corresponding state in the processing link; An image creation module 130, used to create a depth map of the workpiece to be measured in the processing link according to the point cloud data; A data analysis module 140 is used to process the depth map by an image depth processing method to obtain attribute information of the workpiece to be measured in the state; The property detection module 150 is used to obtain the detection result of the workpiece to be detected in each processing link according to the property information and the process standard.
[0101] The process detection device 100 provided in the present application adopts the process detection method in the above embodiment, and can solve the technical problem of how to accurately detect the coil winding process more efficiently. Compared with the prior art, the beneficial effects of the process detection device 100 provided in the present application are the same as the beneficial effects of the process detection method provided in the above embodiment, and other technical features in the process detection device 100 are the same as the features disclosed in the above embodiment method, which will not be repeated here.
[0102] The present application provides a process detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the process detection method in the above-mentioned embodiment one.
[0103] Reference below Figure 3, which shows a schematic diagram of the structure of a process detection device suitable for implementing the embodiment of the present application. The process detection device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The process detection equipment shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0104] like Figure 3 As shown, the process detection device 200 may include a processing device 210 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 220 or a program loaded from a storage device 230 to a random access memory (RAM: Random Access Memory) 240. In RAM240, various programs and data required for the operation of the process detection device are also stored. The processing device 210, ROM220 and RAM240 are connected to each other through a bus 250. An input / output (I / O) interface 260 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 260: an input device 270 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 280 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 230 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 290. The communication device 290 can allow the process detection device to communicate with other devices wirelessly or by wire to exchange data. Although the process detection device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.
[0105] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 230, or installed from the ROM 220. When the computer program is executed by the processing device 210, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.
[0106] The process detection device provided by the present application adopts the process detection method in the above embodiment, which can solve the technical problem of how to accurately detect the coil winding process more efficiently. Compared with the prior art, the beneficial effects of the process detection device provided by the present application are the same as the beneficial effects of the process detection method provided by the above embodiment, and the other technical features in the process detection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.
[0107] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.
[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
[0109] The present application also provides a computer program product, including a computer program, which implements the steps of the process detection method as described above when executed by a processor.
[0110] The computer program product provided by the present application can solve the technical problem of how to accurately detect the coil winding process more efficiently. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the process detection method provided by the above embodiment, which will not be repeated here.
[0111] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.
Claims
1. A process detection method, characterized in that: The process detection method comprises: After binding with the workpiece to be tested, obtaining the process standard corresponding to the workpiece to be tested in each processing link; For each processing link, obtaining point cloud data of the workpiece to be measured in the processing link in a corresponding state; Establishing a depth map of the workpiece to be measured in the processing link according to the point cloud data; Processing the depth map by an image depth processing method to obtain attribute information of the workpiece to be measured in the state; According to the attribute information and the process standard, the detection result of the workpiece to be detected in each of the processing links is obtained.
2. The process detection method according to claim 1, characterized in that: The processing link includes a mold detection link, and the workpiece to be detected in the mold detection link is a winding mold; the point cloud data of the workpiece to be detected in the processing link in a corresponding state is obtained, including: According to the mold detection parameters, the laser profiler is controlled to scan the three areas of the winding mold respectively to obtain point cloud data of the winding mold; The step of processing the depth map by an image depth processing method to obtain the property information of the workpiece to be measured in the state includes: Using dynamic threshold segmentation to extract target parts of the three regions in the depth map; Based on the target portion, the verticality of the winding mold and the cross-sectional perimeter deviation values between different target portions are calculated.
3. The process detection method according to claim 1, characterized in that: The processing link includes an insulating cylinder detection link, and the workpiece to be detected in the insulating cylinder detection link is an insulating cylinder; the point cloud data of the workpiece to be detected in the processing link in a corresponding state is obtained, including: According to the detection parameters of the insulating cylinder, the laser profiler is controlled to scan the three areas of the insulating cylinder respectively to obtain point cloud data of the insulating cylinder; The step of processing the depth map by an image depth processing method to obtain the property information of the workpiece to be measured in the state includes: Using a traversal area algorithm and / or a dynamic threshold segmentation algorithm, extracting bottom end surface information, a strut area, and an insulating tube area in the depth map of the insulating tube; Based on the bottom end face information, the strut area and the insulating cylinder area, the insulating cylinder verticality, the strut verticality, the insulating cylinder circumference deviation, the strut spacing, the insulating cylinder outer diameter and the bottom end face deviation size in the insulating cylinder detection link are calculated.
4. The process detection method according to claim 1, characterized in that: The processing link includes a winding link, and the workpiece to be measured in the winding link is a coil being wound; The step of obtaining the point cloud data of the workpiece to be measured in the corresponding state in the processing step includes: During the winding process, the number of winding turns is obtained in real time; Generating a coil winding model composed of point cloud data in real time according to the number of winding turns; The step of establishing a depth map of the workpiece to be measured in the processing link according to the point cloud data includes: Generating a depth map corresponding to the number of winding turns according to the coil winding model; The step of processing the depth map by an image depth processing method to obtain the property information of the workpiece to be measured in the state includes: Determining a winding direction of the coil based on maximum depth values of different areas in the depth map and a preset depth threshold; Using a traversal area algorithm and / or a dynamic threshold segmentation algorithm, extracting a coil area and a strut area in the depth map of the number of winding turns; Performing filtering and downsampling on the coil region and the strut region to obtain a data fitting surface; The surface is fitted based on the data to calculate the coil outer diameter, coil inner diameter, coil radial size, axial protrusion, line segment unevenness and strut spacing.
5. The process detection method according to claim 1, characterized in that: The processing link includes a post-winding link, and the workpiece to be tested in the winding link is a coil that has been wound; The step of obtaining the point cloud data of the workpiece to be measured in the corresponding state in the processing step includes: According to the post-winding detection parameters, the laser profiler is controlled to scan three areas of the wound coil respectively to obtain point cloud data of the wound coil; The step of processing the depth map by an image depth processing method to obtain the property information of the workpiece to be measured in the state includes: A global threshold segmentation algorithm is used to extract a coil area and a pad area from the depth map of the wound coil; Using a dynamic threshold segmentation algorithm and a morphological algorithm, extracting an upper edge line and a lower edge line in the depth map of the insulating cylinder; Based on the upper edge line and the lower edge line, obtaining the height of the coil; Based on the grayscale histogram distribution, the center points of the pad area are connected, and the verticality size of the pad is obtained according to the ratio of the horizontal coordinate deviation and the vertical coordinate deviation of the connecting line.
6. The process detection method according to claim 1, characterized in that: After obtaining the detection result of the workpiece to be tested in each processing link according to the attribute information and the process standard, the method further includes: In any processing link, if the detection result does not meet the corresponding process standard in the processing link, an alarm signal for indicating process abnormality is issued; The acquired attribute information is sent to the server for storage.
7. The process detection method according to any one of claims 2 to 5, characterized in that: For each processing link, obtaining point cloud data of the workpiece to be measured in the processing link in a corresponding state includes: In response to receiving the calibration instruction, controlling the laser profiler to move so that the laser calibration line follows the movement; When the laser calibration line is detected to move to a specified position, the laser profiler is controlled to stop moving to complete the calibration; When the laser profiler is calibrated, for each processing link, point cloud data of the workpiece to be measured in the corresponding state in the processing link is obtained.
8. A process detection device, characterized in that: The process detection device comprises: A standard acquisition module, used to acquire the process standard corresponding to the workpiece to be tested in each processing link after being bound to the workpiece to be tested; A point cloud acquisition module, used for acquiring point cloud data of the workpiece to be measured in a corresponding state in each processing link; An image creation module, used to create a depth map of the workpiece to be measured in the processing link according to the point cloud data; A data analysis module, used for processing the depth map by an image depth processing method to obtain attribute information of the workpiece to be measured in the state; The property detection module is used to obtain the detection result of the workpiece to be detected in each processing link according to the property information and the process standard.
9. A process detection device, characterized in that: The process detection device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the process detection method according to any one of claims 1 to 7.
10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the process detection method according to any one of claims 1 to 7 are implemented.