Plasma spray coating multi-position fixed-point spraying control system

Abstract

The application discloses a kind of plasma melting coating multi-position fixed-point spraying control system, it is related to the field of spraying processing, system includes multi-class workpiece image acquisition identification module, multi-class workpiece surface state quality analysis module, workpiece defect area quality quantization grading evaluation module, candidate spraying area self-adapting correction module, artificial confirmation spraying execution control module and administrator terminal, according to the construction workpiece multi-class spraying area database, based on visual system automatic identification workpiece and generate spraying path, while collecting the surface state of different workpieces to carry out defect identification, after the quality grading of each defect area identified, according to defect grade, execute spraying area differentiation correction, the quantization grading result of the surface defect of the workpiece to be sprayed is linked with spraying area planning, realize the self-adapting correction of the spraying area of different defects of the workpiece to be sprayed, improve workpiece spraying intelligent efficiency.

Description

Technical Field

[0001] This invention relates to the field of spray coating, specifically a multi-position fixed-point spray coating control system for plasma spray coating. Background Technology

[0002] Plasma spraying, as an important surface engineering technology, is widely used in aerospace, machinery manufacturing, energy and chemical industries to prepare functional coatings such as wear-resistant, corrosion-resistant, thermal barrier, and conductive coatings.

[0003] Currently, for mass-produced standard workpieces, the operator typically pre-teachs the robot's motion trajectory or uses the workpiece's CAD model for offline programming. While simple, this method lacks flexibility. When incoming workpieces exhibit individual differences, such as surface defects caused by processing errors, transportation bumps, or prior use, fixed paths cannot detect and avoid these unsuitable areas for painting. This leads to expensive painting materials being wasted on poor substrates, resulting in coatings with poor adhesion, prone to premature failure, and masking potential workpiece quality issues, posing a serious threat to the reliability of the final product. Existing vision inspection systems, even if they can identify surface defects, typically only perform simple binary judgments of "pass" and "fail," or simply issue a warning to the operator, failing to intelligently link the location, size, and type of defects with subsequent painting path planning. The system cannot automatically and accurately remove severely defective areas from the preset painting area, nor can it intelligently handle minor flaws that do not affect use.

[0004] This application aims to construct a multi-category workpiece spraying area database, automatically identify workpieces and generate spraying paths based on a vision system, collect surface conditions of different workpieces for defect identification, quantitatively evaluate and classify the quality of each identified defect area, and perform differentiated correction of spraying areas according to the defect level. By linking the quantitative classification results of the surface defects of the workpiece to be sprayed with the spraying area planning, adaptive correction of the spraying area of ​​the workpiece to be sprayed with different defects can be achieved, thereby improving the intelligent efficiency of workpiece spraying. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-position fixed-point spraying control system for plasma spray coating to solve the problems in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A plasma spray coating multi-position fixed-point spraying control system includes a multi-type workpiece image acquisition and recognition module, a multi-type workpiece surface condition quality analysis module, a workpiece defect area quality quantitative grading and evaluation module, a candidate spraying area adaptive correction module, a manual confirmation spraying execution control module, and an administrator terminal.

[0008] The multi-category workpiece image acquisition and recognition module acquires workpiece information corresponding to different categories of workpieces to be sprayed by the spraying equipment, constructs a multi-category workpiece spraying area database, acquires workpiece image information to be sprayed, performs database matching, and identifies the workpiece category to be sprayed and candidate spraying areas.

[0009] The multi-category workpiece surface condition quality analysis module acquires surface images of the workpiece to be coated, performs surface quality analysis on the acquired images, identifies the state of the workpiece surface, extracts the defect areas inside the surface condition images of the workpiece to be coated, constructs a structured defect list for each defect area, generates a defect ID, and identifies and provides feedback on the defect category.

[0010] The workpiece defect area quality quantitative grading assessment module performs quantitative assessment of defect size characteristics and grayscale characteristics based on the identified defect areas, analyzes the degree of grayscale fluctuation in the defect areas, constructs a quality grade framework for the surface state of multiple types of workpieces to be coated, and records and provides feedback on different workpieces to be coated.

[0011] The candidate spraying area adaptive correction module obtains the quality level corresponding to the workpiece to be sprayed, and at the same time calls the candidate spraying area corresponding to the workpiece to be sprayed. Based on different level correction methods, it performs defect area classification operation analysis on the workpiece to be sprayed.

[0012] The candidate spraying area adaptive correction module includes a workpiece surface quality defect classification decision execution submodule and a workpiece surface intermediate defect multi-point rejection submodule. The workpiece surface quality defect classification decision execution submodule acquires several sub-images of slight defect markers, intermediate defect markers and severe defect markers inside different collected workpiece surface state images. It pre-acquires the workpiece surface state image with severe defect marker sub-images, does not perform spraying operation on the corresponding workpiece, automatically replaces it as the next workpiece to be detected, and sends alarm information to the administrator terminal at the same time.

[0013] The system acquires a surface state image of the workpiece to be sprayed that contains intermediate defect marker sub-images. It checks whether the workpiece to be sprayed position and the position of the intermediate defect marker sub-image within the workpiece surface state image overlap. If they do not overlap, the workpiece to be sprayed position remains unchanged, and a prompt message is generated for the intermediate defect marker sub-image within the workpiece surface state image. If the current workpiece to be sprayed position overlaps with the position of the intermediate defect marker image, the workpiece data to be sprayed is sent to the workpiece surface intermediate defect multi-point rejection sub-module.

[0014] Acquire a surface state image of the workpiece to be sprayed that contains a minor defect marker image, keep the workpiece to be sprayed position unchanged, and generate a prompt message for the minor defect marker image inside the current surface state image.

[0015] The multi-point rejection submodule for intermediate defects on the workpiece surface screens the number of overlapping sub-images containing intermediate defect markers with the current workpiece's spraying location in the workpiece surface condition image. It then obtains the total actual physical area of ​​the overlapping sub-images and compares this total with the area of ​​the current workpiece's spraying location, setting the total actual physical area within the overlapping sub-images as a threshold. The area of ​​the workpiece to be coated is currently [area missing]. ,like In the current workpiece's area to be coated, the locations of sub-images indicating intermediate defects are removed, and the new area to be coated on the current workpiece is obtained. The minimum limit area threshold for workpiece spraying is preset for the current equipment. Feedback is provided by re-marking severe defects in the surface condition image of the workpiece to be sprayed;

[0016] The manual confirmation spraying execution control module obtains the operation analysis plan for the defect area of ​​the workpiece to be sprayed and performs manual confirmation. After confirmation, it obtains the defect type, quality grade and warning mark of the corresponding workpiece category for the operation analysis plan and archives and enters it.

[0017] Further configuration: The multi-category workpiece image acquisition and recognition module pre-acquires historical workpiece data from the spraying equipment, acquires image information and standard 3D models of different types of workpieces from the historical sprayed workpiece information data, and simultaneously acquires predefined standard spraying area information and spraying process parameters corresponding to different types of workpieces. Based on the image information of different types of workpieces, their corresponding standard spraying area information and spraying process parameter data, a multi-category workpiece spraying area database is constructed.

[0018] The multi-category workpiece image acquisition and recognition module includes several vision sensors, a workpiece image feature matching and recognition submodule, and a multi-category workpiece candidate spraying area corresponding submodule. The vision sensors acquire image data of the workpiece to be sprayed from different angles and send it to the workpiece image feature matching and recognition submodule. This submodule preprocesses the acquired images of the workpiece to be sprayed, obtaining the external contour features and workpiece area features of the workpiece area from the image data. These features are then matched with image data uploaded from different categories of workpieces within the multi-category workpiece spraying area database to identify the specific category of the workpiece to be sprayed. The multi-category workpiece candidate spraying area corresponding submodule sends the identified specific category of the workpiece to the multi-category workpiece spraying area database for retrieval, obtaining the standard spraying area information corresponding to the workpiece category, marking it as a preset candidate spraying area, and simultaneously obtaining the spraying parameter instruction package of the workpiece to be sprayed for upload confirmation.

[0019] Further configuration: The multi-category workpiece surface condition quality analysis module includes a workpiece image surface condition feature extraction submodule and a defect area condition location data feedback submodule. The workpiece image surface condition feature extraction submodule acquires image data of the workpiece to be sprayed, performs grayscale conversion and preprocessing, acquires image data of the workpieces to be sprayed from the multi-category spraying area database, and marks them as the reference image of the workpiece to be sprayed. The preprocessed surface condition image of the workpiece to be sprayed is compared with the reference image of the workpiece to be sprayed, and the significant difference areas between the two images are screened. The significant difference areas on the workpiece surface are extracted and marked as defect areas. The marked areas inside the surface condition image of the workpiece to be sprayed are extracted and bounded by a minimum bounding rectangle for marking. The coordinates of each corner of the bounding rectangle inside the image are obtained. The total number of pixels of each defect area in the surface condition image of the workpiece to be sprayed is collected and marked as the actual physical surface of the defect area. The system records the data. The defect area status location data feedback submodule acquires the surface status image of the workpiece to be coated that has defect area markings. It pre-uploads a defect comparison database of different workpieces through the administrator terminal. The surface status image of the workpiece to be coated with the defect area markings is compared with the defect comparison database of different workpieces to confirm the defect type at the outer rectangle position of the markings in the surface status image of the workpiece to be coated. If there is a surface status image of the workpiece to be coated where the defect type cannot be determined, it is sent to the administrator terminal for manual confirmation. A structured defect list is constructed for each outer rectangle position of the markings in the surface status image of the workpiece to be coated. The structured defect list obtains the defect image inside each outer rectangle position of the markings and generates a defect ID. The defect ID includes the image acquisition time, defect category, actual physical area of ​​the defect area, and coordinates of the outer rectangle bounding box position. The defect ID of each defect area inside the surface status image of the workpiece to be coated is fed back to the administrator terminal for backup.

[0020] Further configuration: The workpiece defect area quality quantification and grading assessment module includes a workpiece surface condition quality quantification analysis submodule and a workpiece surface condition multi-level grading submodule. The workpiece surface condition quality quantification analysis submodule acquires the surface condition image of the workpiece to be coated, which contains defect area markings. It then clips the bounding rectangle of the inner area of ​​the workpiece surface condition image to obtain several sub-images. The total number of pixels in each sub-image is obtained, and the percentage of the sub-image's area to the entire workpiece surface condition image is calculated. When the percentage of the sub-image's area to the entire workpiece surface condition image is greater than or equal to a set defect percentage threshold, the sub-image is marked.

[0021] The total number of pixels in the defect region within the marked sub-image and the total number of pixels in the sub-image are detected separately. The total number of pixels in the defect region within the marked sub-image is set to [value missing]. The total number of pixels in the sub-image is ,when For sub-images whose percentage of the area relative to the entire surface condition image of the workpiece to be coated is greater than or equal to a set pixel percentage threshold, severe defects are marked. If the percentage of the pixel ratio is less than the set threshold, the marked sub-images that are determined to be more than the set defect ratio threshold are predicted to be marked as serious defects.

[0022] If the area of ​​a certain sub-image is less than the area of ​​the corresponding surface condition image of the workpiece to be sprayed compared to a set defect ratio threshold, it is marked as a sub-image to be detected. Several sub-images to be detected and sub-images predicting severe defects within the surface condition image of the workpiece to be sprayed are then combined and marked a second time as valid sub-images. The average gray level of each valid sub-image region is calculated, and a certain valid sub-image is defined as... Monitoring a specific effective sub-image is The grayscale values ​​at different coordinate points are used to define the effective sub-image's grayscale value at a certain coordinate point. Set the average gray level of the effective sub-image region to be [value]. According to the formula:

[0023]

[0024] The average gray value of each effective sub-image is calculated. The reference gray value of the workpiece to be sprayed is preset through the administrator terminal. The average gray value of different effective sub-images is compared with the reference gray value. If the average gray value of different effective sub-images is greater than the reference gray value, the effective sub-image is marked as doubtful for spraying. If the average gray value of different effective sub-images is less than the reference gray value, it indicates that the defect area inside the effective sub-image is not suitable for spraying, and the effective sub-image is marked as unsuitable for spraying.

[0025] Based on the average grayscale value of each valid sub-image, valid sub-images with suspected spraying marks are selected. The grayscale variations within each valid sub-image with suspected spraying marks are analyzed, and the standard deviation of the grayscale values ​​within each valid sub-image is analyzed, which represents the degree of grayscale fluctuation within each valid sub-image. A standard deviation of grayscale values ​​within a given valid sub-image is then defined. According to the formula:

[0026]

[0027] The grayscale fluctuation level within each valid sub-image is calculated. A baseline standard deviation threshold range for the workpiece to be coated is preset through the administrator terminal. If the grayscale fluctuation level within a valid sub-image is greater than the maximum value of the preset baseline standard deviation threshold range, the texture of the defect area within the valid sub-image is determined to be complex, and the valid sub-image is marked as unsuitable for coating. If the grayscale fluctuation level within a valid sub-image is less than the minimum value of the preset baseline standard deviation threshold range, the valid sub-image is marked as questionable for secondary coating. The valid sub-images marked as questionable for secondary coating are sent to the administrator terminal for manual judgment on whether the defect area within the valid sub-image is suitable for coating.

[0028] The multi-level grading submodule for workpiece surface condition pre-summarizes valid sub-images that have not been marked with spraying doubts and are unsuitable for spraying, marks them as minor defects, obtains valid sub-images marked with secondary spraying doubts, and pre-marks them as intermediate defects. If, after manual judgment, there are valid sub-images unsuitable for spraying among the valid sub-images marked with secondary spraying doubts, they are removed, and the unsuitable spraying marking is redone. Valid sub-images with unsuitable spraying markings are then marked as serious defects. The sub-images with minor, intermediate, and serious defects within the different collected images of the workpiece surface condition to be sprayed are statistically summarized, backed up, and uploaded to the administrator terminal.

[0029] Further configuration: The manual confirmation spraying execution control module includes a spraying operation approval and correction execution submodule and a multi-category workpiece visual identification record archiving submodule. The spraying operation approval and correction execution submodule obtains the spraying operation plan and prompt information corresponding to several minor defect markers, medium defect markers and severe defect markers inside the surface state images of different workpieces to be sprayed, and sends them to the administrator terminal. After manual confirmation, the final workpiece spraying area plan is generated. The multi-category workpiece visual identification record archiving submodule is used to summarize the defect data of different workpieces to be sprayed, analyze the proportion of each defect category, record it in the workpiece quality log, and upload it to the administrator terminal for backup.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: by constructing a multi-category spraying area database for workpieces, the workpieces are automatically identified and spraying paths are generated based on a vision system. At the same time, the surface states of different workpieces are collected for defect identification. Each identified defect area is quantitatively evaluated and graded for quality. Based on the defect level, the spraying area is differentiated and corrected. The quantitative grading results of the surface defects of the workpiece to be sprayed are linked with the spraying area planning to achieve adaptive correction of the spraying area of ​​the workpiece to be sprayed for different defects, thereby improving the intelligent efficiency of workpiece spraying. Attached Figure Description

[0031] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0032] Figure 1 This is a schematic diagram of the specific module structure of a plasma spray coating multi-position fixed-point spraying control system according to the present invention;

[0033] Figure 2 This is a flowchart illustrating the implementation of a multi-position fixed-point spraying control system for plasma spray coating according to the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see Figures 1-2 In the embodiments of the present invention, such as Figure 1 , Figure 2 As shown, a plasma spray coating multi-position fixed-point spraying control system includes a multi-type workpiece image acquisition and recognition module, a multi-type workpiece surface condition quality analysis module, a workpiece defect area quality quantitative grading evaluation module, a candidate spraying area adaptive correction module, a manual confirmation spraying execution control module, and an administrator terminal.

[0036] The multi-category workpiece image acquisition and recognition module acquires workpiece information corresponding to different categories of workpieces to be sprayed by the spraying equipment, constructs a multi-category workpiece spraying area database, acquires workpiece image information to be sprayed, performs database matching, and identifies the workpiece category to be sprayed and candidate spraying areas.

[0037] It needs to be explained in detail that the multi-category workpiece image acquisition and recognition module pre-acquires historical workpiece data from the spraying equipment, acquires image information and standard 3D models of different types of workpieces from the historical sprayed workpiece information data, and simultaneously acquires predefined standard spraying area information and spraying process parameters corresponding to different types of workpieces. Based on the image information of different types of workpieces, their corresponding standard spraying area information and spraying process parameter data, a multi-category workpiece spraying area database is constructed.

[0038] The multi-category workpiece image acquisition and recognition module includes several vision sensors, a workpiece image feature matching and recognition submodule, and a multi-category workpiece candidate spraying area corresponding submodule. The vision sensors acquire image data of the workpiece to be sprayed from different angles and send it to the workpiece image feature matching and recognition submodule. This submodule preprocesses the acquired images of the workpiece to be sprayed, obtaining the external contour features and workpiece area features of the workpiece area from the image data. These features are then matched with image data uploaded from different categories of workpieces within the multi-category workpiece spraying area database to identify the specific category of the workpiece to be sprayed. The multi-category workpiece candidate spraying area corresponding submodule sends the identified specific category of the workpiece to the multi-category workpiece spraying area database for retrieval, obtaining the standard spraying area information corresponding to the workpiece category, marking it as a preset candidate spraying area, and simultaneously obtaining the spraying parameter instruction package of the workpiece to be sprayed for upload confirmation.

[0039] The multi-category workpiece surface condition quality analysis module acquires surface images of the workpiece to be coated, performs surface quality analysis on the acquired images, identifies the state of the workpiece surface, extracts the defect areas inside the surface condition images of the workpiece to be coated, constructs a structured defect list for each defect area, generates a defect ID, and identifies and provides feedback on the defect category.

[0040] Further explanation is needed: the multi-category workpiece surface condition quality analysis module includes a workpiece image surface condition feature extraction submodule and a defect area condition location data feedback submodule. The workpiece image surface condition feature extraction submodule acquires image data of the workpiece to be coated, performs grayscale conversion and preprocessing, and obtains image data of the workpieces to be coated from the multi-category coating area database, marking them as the baseline image of the workpiece to be coated. The preprocessed surface condition image of the workpiece to be coated is compared with the baseline image, screening for significant differences between the two images. Significantly different areas on the workpiece surface are extracted and marked as defect areas. The marked areas within the surface condition image of the workpiece to be coated are extracted and bounded by a minimum bounding rectangle. The coordinates of each corner of the bounding rectangle within the image are obtained. The total number of pixels for each defect area in the surface condition image of the workpiece to be coated is collected and marked as the actual physical defect area. The area is recorded. The defect area status location data feedback submodule obtains the surface status image of the workpiece to be sprayed with defect area markings. It pre-uploads a defect comparison database of different workpieces through the administrator terminal. The surface status image of the workpiece to be sprayed with the defect area markings is compared with the defect comparison database of different workpieces to confirm the defect type at the outer rectangle position of the marking inside the surface status image of the workpiece to be sprayed. If there is a surface status image of the workpiece to be sprayed with an undetermined defect type, it is sent to the administrator terminal for manual confirmation. A structured defect list is built for each outer rectangle position of the marking inside the surface status image of the workpiece to be sprayed. The structured defect list obtains the defect image inside each outer rectangle position of the marking and generates a defect ID. The defect ID includes the image acquisition time, defect category, actual physical area of ​​the defect area, and position coordinates of the outer rectangle bounding box. The defect ID of each defect area inside the surface status image of the workpiece to be sprayed is fed back to the administrator terminal for backup.

[0041] The workpiece defect area quality quantitative grading assessment module performs quantitative assessment of defect size characteristics and grayscale characteristics based on the identified defect areas, analyzes the degree of grayscale fluctuation in the defect areas, constructs a quality grade framework for the surface state of multiple types of workpieces to be coated, and records and provides feedback on different workpieces to be coated.

[0042] To be specific, the workpiece defect area quality quantification and grading assessment module includes a workpiece surface condition quality quantification analysis submodule and a workpiece surface condition multi-level grading submodule. The workpiece surface condition quality quantification analysis submodule acquires an image of the workpiece surface condition to be coated that contains defect area markers. It then crops the bounding rectangle within the image to obtain several sub-images, acquires the total number of pixels in each sub-image, and calculates the percentage of the sub-image's area relative to the entire workpiece surface condition image. If the percentage of a sub-image's area relative to the entire workpiece surface condition image is greater than or equal to a set defect percentage threshold, the sub-image is marked.

[0043] The total number of pixels in the defect region within the marked sub-image and the total number of pixels in the sub-image are detected separately. The total number of pixels in the defect region within the marked sub-image is set to [value missing]. The total number of pixels in the sub-image is ,when For sub-images whose percentage of the area relative to the entire surface condition image of the workpiece to be coated is greater than or equal to a set pixel percentage threshold, severe defects are marked. If the percentage of the pixel ratio is less than the set threshold, the marked sub-images that are determined to be more than the set defect ratio threshold are predicted to be marked as serious defects.

[0044] If the area of ​​a certain sub-image is less than the area of ​​the corresponding surface condition image of the workpiece to be sprayed compared to a set defect ratio threshold, it is marked as a sub-image to be detected. Several sub-images to be detected and sub-images predicting severe defects within the surface condition image of the workpiece to be sprayed are then combined and marked a second time as valid sub-images. The average gray level of each valid sub-image region is calculated, and a certain valid sub-image is defined as... Monitoring a specific effective sub-image is The grayscale values ​​at different coordinate points are used to define the effective sub-image's grayscale value at a certain coordinate point. Set the average gray level of the effective sub-image region to be [value]. According to the formula:

[0045]

[0046] The average gray value of each effective sub-image is calculated. The reference gray value of the workpiece to be sprayed is preset through the administrator terminal. The average gray value of different effective sub-images is compared with the reference gray value. If the average gray value of different effective sub-images is greater than the reference gray value, the effective sub-image is marked as doubtful for spraying. If the average gray value of different effective sub-images is less than the reference gray value, it indicates that the defect area inside the effective sub-image is not suitable for spraying, and the effective sub-image is marked as unsuitable for spraying.

[0047] Based on the average grayscale value of each valid sub-image, valid sub-images with suspected spraying marks are selected. The grayscale variations within each valid sub-image with suspected spraying marks are analyzed, and the standard deviation of the grayscale values ​​within each valid sub-image is analyzed, which represents the degree of grayscale fluctuation within each valid sub-image. A standard deviation of grayscale values ​​within a given valid sub-image is then defined. According to the formula:

[0048]

[0049] The grayscale fluctuation level within each valid sub-image is calculated. A baseline standard deviation threshold range for the workpiece to be coated is preset through the administrator terminal. If the grayscale fluctuation level within a valid sub-image is greater than the maximum value of the preset baseline standard deviation threshold range, the texture of the defect area within the valid sub-image is determined to be complex, and the valid sub-image is marked as unsuitable for coating. If the grayscale fluctuation level within a valid sub-image is less than the minimum value of the preset baseline standard deviation threshold range, the valid sub-image is marked as questionable for secondary coating. The valid sub-images marked as questionable for secondary coating are sent to the administrator terminal for manual judgment on whether the defect area within the valid sub-image is suitable for coating.

[0050] Further explanation is needed. The multi-level classification sub-module for workpiece surface condition pre-summarizes the valid sub-images that have not been marked for doubtful spraying and those that are not suitable for spraying, marks them for minor defects, obtains the valid sub-images marked for doubtful spraying, and pre-marks them for intermediate defects. If, after manual judgment, there are valid sub-images marked for doubtful spraying that are not suitable for spraying, they are removed, and the unsuitable spraying marking is redone. The valid sub-images with unsuitable spraying markings are then marked for serious defects. The sub-images of minor, intermediate, and serious defects within the different collected images of the workpiece surface condition to be sprayed are statistically summarized, backed up, and uploaded to the administrator terminal.

[0051] The candidate spraying area adaptive correction module obtains the quality level corresponding to the workpiece to be sprayed, and at the same time calls the candidate spraying area corresponding to the workpiece to be sprayed. Based on different level correction methods, it performs defect area classification operation analysis on the workpiece to be sprayed.

[0052] It needs to be explained in detail that the candidate spraying area adaptive correction module includes a workpiece surface quality defect classification decision execution submodule and a workpiece surface intermediate defect multi-point rejection submodule. The workpiece surface quality defect classification decision execution submodule acquires several sub-images of slight defect markers, intermediate defect markers and severe defect markers inside the collected surface state images of different workpieces to be sprayed. It pre-acquires the surface state image of the workpiece to be sprayed that contains the sub-image of severe defect markers, does not perform spraying operation on the corresponding workpiece, automatically replaces it as the next workpiece to be detected, and sends alarm information to the administrator terminal at the same time.

[0053] The system acquires a surface state image of the workpiece to be sprayed that contains intermediate defect marker sub-images. It checks whether the workpiece to be sprayed position and the position of the intermediate defect marker sub-image within the workpiece surface state image overlap. If they do not overlap, the workpiece to be sprayed position remains unchanged, and a prompt message is generated for the intermediate defect marker sub-image within the workpiece surface state image. If the current workpiece to be sprayed position overlaps with the position of the intermediate defect marker image, the workpiece data to be sprayed is sent to the workpiece surface intermediate defect multi-point rejection sub-module.

[0054] Acquire a surface state image of the workpiece to be sprayed that contains a minor defect marker image, keep the workpiece to be sprayed position unchanged, and generate a prompt message for the minor defect marker image inside the current surface state image.

[0055] The multi-point rejection submodule for intermediate defects on the workpiece surface screens the number of overlapping sub-images containing intermediate defect markers with the current workpiece's spraying location in the workpiece surface condition image. It then obtains the total actual physical area of ​​the overlapping sub-images and compares this total with the area of ​​the current workpiece's spraying location, setting the total actual physical area within the overlapping sub-images as a threshold. The area of ​​the workpiece to be coated is currently [area missing]. ,like In the current workpiece's area to be coated, the locations of sub-images indicating intermediate defects are removed, and the new area to be coated on the current workpiece is obtained. The minimum limit area threshold for workpiece spraying is preset for the current equipment. The image of the surface condition of the workpiece to be sprayed is used to re-mark serious defects and provide feedback.

[0056] The manual confirmation spraying execution control module obtains the operation analysis plan for the defect area of ​​the workpiece to be sprayed and performs manual confirmation. After confirmation, it obtains the defect type, quality grade and warning mark of the corresponding workpiece category for the operation analysis plan and archives and enters it.

[0057] It needs to be explained in detail that the manual confirmation spraying execution control module includes a spraying operation approval and correction execution submodule and a multi-category workpiece visual identification record archiving submodule. The spraying operation approval and correction execution submodule obtains the spraying operation plan and prompt information corresponding to several minor defect marks, medium defect marks and severe defect marks inside the surface state images of different workpieces to be sprayed, and sends them to the administrator terminal. After manual confirmation, the final workpiece spraying area plan is generated. The multi-category workpiece visual identification record archiving submodule is used to summarize the defect data of different workpieces to be sprayed, analyze the proportion of each defect category, record it in the workpiece quality log, and upload it to the administrator terminal for backup.

[0058] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A multi-position fixed-point spraying control system for plasma spray coating, characterized in that: The system includes a multi-category workpiece image acquisition and recognition module, a multi-category workpiece surface condition quality analysis module, a workpiece defect area quality quantitative grading and evaluation module, a candidate spraying area adaptive correction module, a manual confirmation spraying execution control module, and an administrator terminal. The multi-category workpiece image acquisition and recognition module acquires the workpiece image information to be sprayed from the spraying equipment, performs database matching, and identifies the type of workpiece to be sprayed and candidate spraying areas; The multi-category workpiece surface condition quality analysis module acquires surface images of the workpiece to be coated, performs surface quality analysis on the acquired images, identifies the state of the workpiece surface, extracts the defect areas inside the surface condition images of the workpiece to be coated, and constructs a structured defect list for each defect area. The workpiece defect area quality quantitative grading assessment module performs quantitative assessment of defect size characteristics and grayscale characteristics based on the identified defect areas, analyzes the degree of grayscale fluctuation in the defect areas, and constructs a quality grade framework for the surface state of multiple types of workpieces to be coated. The candidate spraying area adaptive correction module obtains the quality level corresponding to the workpiece to be sprayed, and calls the candidate spraying area corresponding to the workpiece to be sprayed. Based on different level correction methods, it performs defect area classification operation analysis on the workpiece to be sprayed. The candidate spraying area adaptive correction module includes a workpiece surface quality defect classification decision execution submodule and a workpiece surface medium-level defect multi-point rejection submodule. The workpiece surface quality defect classification decision execution submodule obtains several sub-images of slight defect markers, medium defect markers and severe defect markers inside the collected surface state images of different workpieces to be sprayed. It pre-obtains the surface state image of the workpiece to be sprayed that contains the sub-image of severe defect markers, does not perform spraying operation on the corresponding workpiece, and automatically replaces it as the next workpiece to be detected, while sending alarm information to the administrator terminal. The system acquires a surface state image of the workpiece to be sprayed that contains intermediate defect marker sub-images. It checks whether the workpiece to be sprayed position and the position of the intermediate defect marker sub-image within the workpiece surface state image overlap. If they do not overlap, the workpiece to be sprayed position remains unchanged, and a prompt message is generated for the intermediate defect marker sub-image within the workpiece surface state image. If the current workpiece to be sprayed position overlaps with the position of the intermediate defect marker image, the workpiece data to be sprayed is sent to the workpiece surface intermediate defect multi-point rejection sub-module. Acquire a surface state image of the workpiece to be sprayed that contains a minor defect marker image, keep the workpiece to be sprayed position unchanged, and generate a prompt message for the minor defect marker image inside the current surface state image. The multi-point rejection submodule for intermediate defects on the workpiece surface screens the number of overlapping sub-images containing intermediate defect markers with the current workpiece's spraying location in the workpiece surface condition image. It then obtains the total actual physical area of ​​the overlapping sub-images and compares this total with the area of ​​the current workpiece's spraying location, setting the total actual physical area within the overlapping sub-images as a threshold. The area of ​​the workpiece to be coated is currently [area missing]. ,like In the current workpiece's area to be coated, the locations of sub-images indicating intermediate defects are removed, and the new area to be coated on the current workpiece is obtained. The minimum limit area threshold for workpiece spraying is preset for the current equipment. Feedback is provided by re-marking severe defects in the surface condition image of the workpiece to be sprayed; The manual confirmation spraying execution control module obtains the operation analysis plan for the defect area of ​​the workpiece to be sprayed and performs manual confirmation. After confirmation, the operation analysis plan is obtained and archived.

2. The plasma spray coating multi-position fixed-point spraying control system according to claim 1, characterized in that... The multi-category workpiece image acquisition and recognition module pre-acquires historical workpiece data from the spraying equipment, acquires image information and standard 3D models of different types of workpieces from the historical sprayed workpiece information data, and simultaneously acquires predefined standard spraying area information and spraying process parameters corresponding to different types of workpieces. Based on the image information of different types of workpieces, their corresponding standard spraying area information and spraying process parameter data, a multi-category workpiece spraying area database is constructed. The multi-category workpiece image acquisition and recognition module includes several vision sensors, a workpiece image feature matching and recognition submodule, and a multi-category workpiece candidate spraying area corresponding submodule. The vision sensors acquire image data of the workpiece to be sprayed from different angles and send it to the workpiece image feature matching and recognition submodule. This submodule preprocesses the acquired images of the workpiece to be sprayed, obtaining the external contour features and workpiece area features of the workpiece area from the image data. These features are then matched with image data uploaded from different categories of workpieces within the multi-category workpiece spraying area database to identify the specific category of the workpiece to be sprayed. The multi-category workpiece candidate spraying area corresponding submodule sends the identified specific category of the workpiece to the multi-category workpiece spraying area database for retrieval, obtaining the standard spraying area information corresponding to the workpiece category, marking it as a preset candidate spraying area, and simultaneously obtaining the spraying parameter instruction package of the workpiece to be sprayed for upload confirmation.

3. A multi-position fixed-point spraying control system for plasma spray coating according to claim 1, characterized in that... The multi-category workpiece surface condition quality analysis module includes a workpiece image surface condition feature extraction submodule and a defect area condition location data feedback submodule. The workpiece image surface condition feature extraction submodule acquires the image data of the workpiece to be sprayed, performs grayscale conversion and preprocessing, acquires the image data of the workpieces to be sprayed in the multi-category spraying area database, and marks them as the reference workpiece image to be sprayed. The preprocessed workpiece surface condition image to be sprayed is compared with the reference workpiece image to be sprayed, and the significant difference areas between the workpiece surface condition image to be sprayed and the reference workpiece image to be sprayed are screened. The significant difference areas on the workpiece surface are extracted and marked as defect areas. The marked areas inside the workpiece surface condition image to be sprayed are extracted and bounded by the minimum bounding rectangle bounding box for marking. The coordinates of each side corner of the bounding rectangle inside the image are obtained. The total number of pixels of each defect area in the workpiece surface condition image to be sprayed is collected and marked as the actual physical area of ​​the defect area for recording.

4. A multi-position fixed-point spraying control system for plasma spray coating according to claim 3, characterized in that... The defect area status location data feedback submodule acquires surface status images of workpieces to be coated that have defect area markings. It pre-uploads a database of defects for different workpieces via an administrator terminal, comparing the surface status images of the workpieces to be coated with the database to confirm the defect type at the bounding rectangle position of the marking within the surface status image. If there are surface status images of workpieces to be coated where the defect type cannot be determined, they are sent to the administrator terminal for manual confirmation. A structured defect list is constructed for each bounding rectangle position of the marking within the surface status image of the workpiece to be coated. The structured defect list acquires the defect image within each bounding rectangle position and generates a defect ID. The defect ID includes the image acquisition time, defect type, actual physical area of ​​the defect area, and the coordinates of the bounding rectangle's position. The defect ID of each defect area within the surface status image of the workpiece to be coated is fed back to the administrator terminal for backup.

5. A multi-position fixed-point spraying control system for plasma spray coating according to claim 1, characterized in that... The workpiece defect area quality quantification and grading evaluation module includes a workpiece surface condition quality quantification analysis submodule and a workpiece surface condition multi-level grading submodule. The workpiece surface condition quality quantification analysis submodule acquires an image of the workpiece surface condition to be coated that contains defect area markers. It then crops the bounding rectangle within the image to obtain several sub-images, acquires the total number of pixels in each sub-image, and calculates the percentage of the sub-image's area relative to the entire workpiece surface condition image. If the percentage of the sub-image's area relative to the entire workpiece surface condition image is greater than or equal to a set defect percentage threshold, the sub-image is marked. The total number of pixels in the defect region within the marked sub-image and the total number of pixels in the sub-image are detected separately. The total number of pixels in the defect region within the marked sub-image is set to [value missing]. The total number of pixels in the sub-image is ,when For sub-images whose percentage of the area relative to the entire surface condition image of the workpiece to be coated is greater than or equal to a set pixel percentage threshold, severe defects are marked. If the percentage of the pixel ratio is less than the set threshold, the marked sub-images that are determined to be more than the set defect ratio threshold are predicted to be marked as serious defects. If the area of ​​a certain sub-image is less than the area of ​​the corresponding surface condition image of the workpiece to be sprayed, it is marked as a sub-image to be detected. Several sub-images to be detected and sub-images marked with predicted serious defects inside the surface condition image of the workpiece to be sprayed are summarized and marked as valid sub-images. The valid sub-images are then analyzed.

6. A multi-position fixed-point spraying control system for plasma spray coating according to claim 5, characterized in that... The workpiece surface condition quality quantification analysis submodule acquires each effective sub-image, calculates the average gray level of each effective sub-image region, and sets a certain effective sub-image as... Monitoring a specific effective sub-image is The grayscale values ​​at different coordinate points are used to define the effective sub-image's grayscale value at a certain coordinate point. Set the average gray level of the effective sub-image region to be [value]. According to the formula: The average gray value of each effective sub-image is calculated. The reference gray value of the workpiece to be sprayed is preset through the administrator terminal. The average gray value of different effective sub-images is compared with the reference gray value. If the average gray value of different effective sub-images is greater than the reference gray value, the effective sub-image is marked as doubtful for spraying. If the average gray value of different effective sub-images is less than the reference gray value, it indicates that the defect area inside the effective sub-image is not suitable for spraying, and the effective sub-image is marked as unsuitable for spraying. Based on the average grayscale value of each valid sub-image, valid sub-images with suspected spraying marks are selected. The grayscale variations within each valid sub-image with suspected spraying marks are analyzed, and the standard deviation of the grayscale values ​​within each valid sub-image is analyzed, which represents the degree of grayscale fluctuation within each valid sub-image. A standard deviation of grayscale values ​​within a given valid sub-image is then defined. According to the formula: The grayscale fluctuation level within each valid sub-image is calculated. A baseline standard deviation threshold range for the workpiece to be coated is preset through the administrator terminal. If the grayscale fluctuation level within a valid sub-image is greater than the maximum value of the preset baseline standard deviation threshold range, the texture of the defect area within the valid sub-image is determined to be complex, and the valid sub-image is marked as unsuitable for coating. If the grayscale fluctuation level within a valid sub-image is less than the minimum value of the preset baseline standard deviation threshold range, the valid sub-image is marked as questionable for secondary coating. The valid sub-images marked as questionable for secondary coating are sent to the administrator terminal for manual judgment on whether the defect area within the valid sub-image is suitable for coating.

7. A multi-position fixed-point spraying control system for plasma spray coating according to claim 5, characterized in that... The multi-level grading submodule for workpiece surface condition pre-summarizes valid sub-images that have not been marked for suspected spraying and those unsuitable for spraying, marks them for minor defects, obtains valid sub-images marked for suspected secondary spraying, and pre-marks them for intermediate defects. If, after manual judgment, there are valid sub-images unsuitable for spraying among the valid sub-images marked for suspected secondary spraying, they are removed, and unsuitable spraying is marked again. Valid sub-images with unsuitable spraying are then marked for severe defects. Sub-images with minor, intermediate, and severe defects within different collected images of the workpiece surface condition are statistically summarized, backed up, and uploaded to the administrator terminal.

8. A multi-position fixed-point spraying control system for plasma spray coating according to claim 1, characterized in that... The manual confirmation spraying execution control module includes a spraying operation approval and correction execution submodule and a multi-category workpiece visual identification recording and archiving submodule. The spraying operation approval and correction execution submodule acquires the spraying operation plan and prompt information corresponding to several minor defect markers, medium defect markers and severe defect markers inside the surface state images of different workpieces to be sprayed, and sends them to the administrator terminal. After manual confirmation, the final workpiece spraying area plan is generated. The multi-category workpiece visual identification recording and archiving submodule is used to summarize the defect data of different workpieces to be sprayed, analyze the proportion of each defect category, record it in the workpiece quality log, and upload it to the administrator terminal for backup.

Images