CN114912832A - Method and system for evaluating quality of magnesium-titanium alloy surface coating process - Google Patents
Method and system for evaluating quality of magnesium-titanium alloy surface coating process Download PDFInfo
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Abstract
The invention provides a method and a system for evaluating the coating process quality of a magnesium-titanium alloy surface, which relate to the technical field related to data processing, and are characterized in that a coating quality evaluation system is used for collecting application scene data of a coating process to obtain a preset evaluation index parameter; acquiring an image of the surface of the target magnesium-titanium alloy by an image acquisition module to obtain a surface image acquisition set, and performing image identification by using a processing module connected with the surface image acquisition set to generate a color deviation evaluation parameter and a defect evaluation parameter; generating a coating thickness evaluation parameter through a thickness measuring module; the flexibility measuring module is used for obtaining a flexibility measuring image, the processing module is used for processing the flexibility measuring image to generate a flexibility evaluation parameter, and a coating quality evaluation result is generated according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter, so that the technical effects of evaluating the coating process quality in real time and improving the evaluation efficiency and accuracy are achieved.
Description
Technical Field
The invention relates to the technical field related to data processing, in particular to a method and a system for evaluating the coating process quality of a magnesium-titanium alloy surface.
Background
With the development of modern science and technology, the related preparation technical problems and recovery problems which once plague the magnesium-titanium alloy industry have been solved one after another, and the latter process, namely the surface treatment process, of magnesium-titanium alloy products is becoming more and more important.
For the active magnesium titanium, the proper surface treatment can provide the product with protection and decoration and can endow certain special functions, such as moisture in the air, acid-base salt, microorganism, ultraviolet rays and other corrosive media, so that the active magnesium titanium is easily corroded and gradually damaged; the magnesium-titanium alloy workpiece is mainly used for high-quality and high-grade products at present, the decoration of the magnesium-titanium alloy workpiece is very important except for the performance requirement of the magnesium-titanium alloy workpiece, and the product can obtain decorative coatings with different colors, gloss, patterns and the like by applying the diversity of the colors of the coatings and the skill of coating construction, so that the product can have a pleasant feeling, the added value is improved, and the application range of the product is expanded; in addition, the coating formed by special coating with special composition can endow products with certain special properties, such as: fire protection, thermal insulation, radiation protection, and the like.
The coating is the first choice of each manufacturer with the unique advantages of low cost, small investment, good effect, simple process and the like, the existing coating process quality evaluation method mostly depends on experienced engineering technicians for evaluation, the requirements on the quality of the workers are high, the efficiency is low, the real-time evaluation on the coating process quality cannot be achieved, further, the instability of the accuracy of the evaluation result is caused due to different experience degrees of different engineering technicians, how to realize the real-time evaluation on the coating process quality of the magnesium-titanium alloy surface is realized, and the improvement of the evaluation efficiency and the accuracy becomes the technical problem which needs to be solved urgently at present.
Disclosure of Invention
The application provides an evaluation method and system for magnesium-titanium alloy surface coating process quality, which are used for solving the technical problems of how to realize real-time evaluation of magnesium-titanium alloy surface coating process quality and improve evaluation efficiency and accuracy, achieving the real-time evaluation of magnesium-titanium alloy surface coating process quality, improving evaluation efficiency and accuracy, and feeding back in time when a coating process has problems so as to adjust the process and reduce the technical effect of economic loss.
In view of the above problems, the present application provides a method and a system for evaluating the quality of a magnesium-titanium alloy surface coating process.
In a first aspect, an embodiment of the present application provides a method for evaluating quality of a magnesium-titanium alloy surface coating process, where the method is applied to a coating quality evaluation system, and the coating quality evaluation system is communicatively connected to an image acquisition module, a thickness measurement module, a flexibility measurement module, and a processing module, and the method includes: acquiring application scene data of a coating process through the coating quality evaluation system, and performing big data-based evaluation index distribution according to an acquisition result to obtain a preset evaluation index parameter; acquiring an image of the surface of the target magnesium-titanium alloy through the image acquisition module to obtain a surface image acquisition set, and sending the surface image acquisition set to the processing module through the image acquisition module; performing image recognition on the surface image collection set through the processing module, and generating a color deviation evaluation parameter and a defect evaluation parameter according to an image recognition result and the preset evaluation index parameter; measuring the coating thickness of the surface of the target magnesium-titanium alloy through the thickness measuring module to generate a coating thickness measuring parameter, and generating a coating thickness evaluation parameter according to the coating thickness measuring parameter and the preset evaluation index parameter; the flexibility testing module is used for testing the flexibility of the coating of the target magnesium-titanium alloy, the image acquisition module is used for acquiring the image of the surface of the target magnesium-titanium alloy, and the flexibility testing image is sent to the processing module; performing feature recognition on the flexibility measurement image through the processing module, and generating a flexibility evaluation parameter according to a feature recognition result and the preset evaluation index parameter; and generating a coating quality evaluation result of the target magnesium-titanium alloy surface according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter.
In a second aspect, an embodiment of the present application provides a system for evaluating quality of a magnesium titanium alloy surface coating process, where the system includes: the coating quality evaluation system is used for acquiring application scene data of a coating process and performing big data-based evaluation index distribution according to an acquisition result to obtain a preset evaluation index parameter; the image acquisition module is used for acquiring an image of the surface of the target magnesium-titanium alloy to obtain a surface image acquisition set, and the surface image acquisition set is sent to the processing module through the image acquisition module; the processing module is used for carrying out image recognition on the surface image acquisition set and generating a color deviation evaluation parameter and a defect evaluation parameter according to an image recognition result and the preset evaluation index parameter; the thickness measuring module is used for measuring the coating thickness of the surface of the target magnesium-titanium alloy through the thickness measuring module to generate a coating thickness measuring parameter, and a coating thickness evaluation parameter is generated according to the coating thickness measuring parameter and the preset evaluation index parameter; the flexibility measuring module is used for testing the flexibility of a coating of the target magnesium-titanium alloy, acquiring an image of the surface of the target magnesium-titanium alloy through the image acquisition module and sending a flexibility measuring image to the processing module; performing feature recognition on the flexibility measurement image through the processing module, and generating a flexibility evaluation parameter according to a feature recognition result and the preset evaluation index parameter; and the quality evaluation module is used for generating a coating quality evaluation result of the target magnesium-titanium alloy surface according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the evaluation method and the evaluation system for the coating process quality of the magnesium-titanium alloy surface, the coating quality evaluation system is used for collecting application scene data of a coating process, and evaluation index distribution based on big data is carried out according to the collection result to obtain preset evaluation index parameters; acquiring an image of the surface of the target magnesium-titanium alloy through the image acquisition module to obtain a surface image acquisition set, and sending the surface image acquisition set to the processing module through the image acquisition module; performing image recognition on the surface image collection set through the processing module, and generating a color deviation evaluation parameter and a defect evaluation parameter according to an image recognition result and the preset evaluation index parameter; measuring the coating thickness of the surface of the target magnesium-titanium alloy through the thickness measuring module to generate a coating thickness measuring parameter, and generating a coating thickness evaluation parameter according to the coating thickness measuring parameter and the preset evaluation index parameter; the flexibility testing module is used for testing the flexibility of the coating of the target magnesium-titanium alloy, the image acquisition module is used for acquiring the image of the surface of the target magnesium-titanium alloy, and the flexibility testing image is sent to the processing module; performing feature recognition on the flexibility determination image through the processing module, and generating a flexibility evaluation parameter according to a feature recognition result and the preset evaluation index parameter; generating a coating quality evaluation result of the target magnesium-titanium alloy surface according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter; the technical problems of how to realize real-time evaluation of the coating process quality of the magnesium-titanium alloy surface and improving the evaluation efficiency and accuracy are solved, the real-time evaluation of the coating process quality of the magnesium-titanium alloy surface is achieved, the evaluation efficiency and accuracy are improved, and the feedback can be timely carried out when the coating process has problems, so that the process is adjusted, and the economic loss is reduced.
Drawings
FIG. 1 is a schematic flow chart of a method for evaluating the quality of a magnesium-titanium alloy surface coating process provided by the present application;
fig. 2 is a schematic flow chart illustrating image recognition performed on a surface image collection set in the method for evaluating the quality of a magnesium-titanium alloy surface coating process provided by the present application;
fig. 3 is a schematic flow chart of the color deviation evaluation parameter generated in the method for evaluating the coating process quality of the magnesium-titanium alloy surface provided by the present application;
FIG. 4 is a schematic flow chart of coating thickness evaluation parameters generated in the method for evaluating the coating process quality of the magnesium-titanium alloy surface provided by the present application;
FIG. 5 is a schematic structural diagram of a system for evaluating the quality of a magnesium-titanium alloy surface coating process provided by the present application;
description of reference numerals: the coating quality evaluation system 100 comprises an image acquisition module 200, a thickness measuring module 300, a flexibility measuring module 400, a processing module 500 and a quality evaluation module 600.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The application provides an evaluation method and system for magnesium-titanium alloy surface coating process quality, and the method and system are used for solving the technical problems of how to realize real-time evaluation of magnesium-titanium alloy surface coating process quality and improve evaluation efficiency and accuracy, so that the real-time evaluation of magnesium-titanium alloy surface coating process quality is achieved, the evaluation efficiency and accuracy are improved, and the feedback can be timely performed when the coating process has problems, so that the process is adjusted, and the technical effect of reducing economic loss is achieved.
Example one
As shown in fig. 1, the present application provides a method for evaluating the quality of a magnesium-titanium alloy surface coating process, the method is applied to a coating quality evaluation system, the coating quality evaluation system is in communication connection with an image acquisition module, a thickness measurement module, a flexibility measurement module and a processing module, and the method comprises:
s100: acquiring application scene data of a coating process through the coating quality evaluation system, and performing big data-based evaluation index distribution according to an acquisition result to obtain a preset evaluation index parameter;
in particular, coating is favored by manufacturers due to its characteristics of low cost, small investment, good effect, relatively simple process, etc., and becomes an important link in modern product manufacturing processes.
The proper surface treatment can make the product protective and decorative, and can endow some special functions, for the active magnesium-titanium alloy, the protection obtained by the surface technology is crucial, but the magnesium-titanium alloy workpiece also has higher decoration and functionality in the face of more and more intense competition and more demanding customers.
Coating quality is one of important aspects of overall product quality, in the embodiment of the application, application scene data of a coating process is acquired by the coating quality evaluation system, wherein the application scene data may include action object information and coating purpose information of the coating process, and for example, the action object information of the coating process may be whether to coat a vehicle or coat an electronic and electrical product; the coating target information can be protective and decorative properties of the workpiece or special functions of products, such as fire prevention, heat insulation, radiation protection and the like, and evaluation index distribution based on big data is carried out according to the application scene data acquisition result to obtain preset evaluation index parameters; the preset evaluation index parameters comprise coating quality acceptance standards corresponding to different application scenes, and can comprise national standards, industry standards or standards negotiated with customers. The preset evaluation index parameters are obtained, and reference basis is provided for the evaluation of the subsequent coating process quality.
S200: acquiring an image of the surface of the target magnesium-titanium alloy through the image acquisition module to obtain a surface image acquisition set, and sending the surface image acquisition set to the processing module through the image acquisition module;
specifically, in order to realize the evaluation of the process quality of the coating surface of the target magnesium-titanium alloy, the image acquisition module is used for acquiring images of the coating surface of the magnesium-titanium alloy, the images of the surface of the magnesium-titanium alloy can be acquired from multiple angles to obtain a surface image acquisition set, and the image acquisition module is connected with the processing module and is used for sending the surface image acquisition set acquired by the image acquisition module to the processing module for processing.
S300: performing image recognition on the surface image collection set through the processing module, and generating a color deviation evaluation parameter and a defect evaluation parameter according to an image recognition result and the preset evaluation index parameter;
specifically, the processing module is used for carrying out image recognition processing on a surface image collection set collected by the image collection module to obtain an image recognition result, and generating a color deviation evaluation parameter and a defect evaluation parameter according to the image recognition result and the preset evaluation index parameter, wherein the color deviation evaluation parameter is used for evaluating the color of coating, and the defect evaluation parameter is used for evaluating defects such as poor coverage, orange peel, speckling, shrinkage cavity, pinholes and the like in the coating process.
Optionally, as shown in fig. 2, an implementation manner of step S300 in the method provided in the embodiment of the present application includes:
a) evaluating the image light source of the surface image acquisition set through the processing module to obtain a brightness evaluation result;
b) when the brightness evaluation result meets an expected threshold value, mapping the co-located pixel values of the surface image acquisition set and the comparison image into a probability vector through the processing module;
c) and generating the color deviation evaluation parameter according to the difference value of the probability vector and the preset evaluation index parameter.
Specifically, the color of the coating surface is required to be not doped with other colors or be dropped, in order to obtain color deviation evaluation parameters of the coating surface, in the embodiment of the application, the processing module evaluates the image brightness of the surface image acquisition set to obtain a brightness evaluation result, when the brightness evaluation result meets an expected threshold, the expected threshold can be obtained according to historical empirical data, and the processing module maps the co-located pixel values of the surface image acquisition set and the comparison image into a probability vector; the comparison image is a reference image in the preset evaluation index parameters; specifically, each pixel value of the image and the comparison image in the surface image collection set is mapped to be a CN probability vector, and the difference between two vector distributions is calculated to measure the perceptual color difference between the two images, preferably, the calculation method is Wasserstein distance, the difference value of the probability vector is obtained, and the color deviation evaluation parameter is generated through the difference value of the probability vector and the preset evaluation index parameter.
Further, after the processing module performs image light source evaluation on the surface image collection set to obtain a brightness evaluation result, the method further includes:
b1) when the brightness evaluation result does not meet the expected threshold, generating a brightness influence characteristic according to the brightness evaluation result;
b2) performing brightness optimization compensation on the mapping result of the probability vector through the brightness influence characteristics, and generating the color deviation evaluation parameter according to the optimization compensation result;
b3) and generating a feedback brightness control parameter according to the brightness evaluation result, and feeding the feedback brightness control parameter back to the image acquisition module.
Specifically, when the brightness evaluation result does not satisfy the expected threshold, in order to achieve the purpose of obtaining a color deviation evaluation parameter by using brightness, a brightness influence feature is generated according to the brightness evaluation result, brightness is optimized and compensated by using the brightness influence feature on the mapping result of the probability vector, the color deviation evaluation parameter is generated according to the optimization and compensation result, a feedback brightness control parameter is generated according to the brightness evaluation result, the feedback brightness control parameter is fed back to the image acquisition module, the image acquisition module is corrected, and the brightness is optimized and compensated by using the image acquisition module.
Further, as shown in fig. 3, generating the color deviation evaluation parameter according to the difference value of the probability vector and the preset evaluation index parameter includes:
c1) judging whether the difference value meets a preset difference threshold value or not, and when the difference value meets the preset difference threshold value, obtaining another evaluation image of the position corresponding to the difference value, wherein the other evaluation image is different from the surface image collection set in collection angle;
c2) carrying out flatness evaluation on the target area on the other evaluation image to obtain a flatness evaluation parameter;
c3) and when the flatness evaluation parameters are not abnormal, generating the color deviation evaluation parameters through the difference values of the probability vectors and the preset evaluation index parameters.
Specifically, the difference value of two mapping probability vectors of an image in a surface image acquisition set and a comparison image is compared with a preset threshold value, the preset threshold value is obtained according to historical experience data, when the difference value meets the preset difference threshold value, another evaluation image of the image position corresponding to the difference value is obtained, and the another evaluation image is an image obtained at the same position at different acquisition angles; carrying out flatness evaluation on the other evaluation image in a target area to obtain a flatness evaluation parameter, when the evaluation parameter is abnormal, carrying out feature matching on the surface image acquisition set through a defect feature set, comparing a feature matching result with a preset evaluation index parameter, wherein the preset evaluation index parameter comprises information such as defect types and quality levels corresponding to various surface feature defects, and generating the defect evaluation parameter according to a comparison result; when the flatness evaluation parameters are not abnormal, generating the color deviation evaluation parameters through the difference values of the probability vectors and the preset evaluation index parameters; the defect evaluation parameters are obtained.
S400: measuring the coating thickness of the surface of the target magnesium-titanium alloy through the thickness measuring module to generate a coating thickness measuring parameter, and generating a coating thickness evaluation parameter according to the coating thickness measuring parameter and the preset evaluation index parameter;
particularly, the coating thickness with uniform thickness can create conditions for leveling, beautifying and brightening the coating surface; the coating thickness is too low, so that the shielding effect of coating is reduced and the protection effect is poor; the defects of sagging, wrinkling and the like can occur when the coating thickness is too large, the coating thickness is an important index which needs to be controlled in coating construction, and whether the coating thickness is controlled to be reasonable or not directly influences other properties of the coating.
And determining the coating thickness of the surface of the target magnesium-titanium alloy through the thickness determination module to generate a coating thickness determination parameter, and comparing the coating thickness determination parameter with the preset evaluation index parameter to generate a coating thickness evaluation parameter for evaluating the coating surface thickness.
Further, as shown in fig. 4, an implementation manner of step S400 in the method provided in the embodiment of the present application includes:
s410: setting a thickness initial evaluation interval, carrying out thickness deviation value evaluation according to the thickness initial evaluation interval and the coating thickness measurement parameters, and generating thickness deviation value information according to an evaluation result, wherein the thickness deviation value information comprises position information;
s420: performing grouping fitting of the nearest position distance on the positive thickness deviation value and the negative thickness deviation value in the thickness deviation value information to obtain a grouping fitting result;
s430: and generating a coating thickness evaluation parameter according to the grouping fitting result and the preset evaluation index parameter.
Specifically, setting a thickness initial evaluation interval according to the preset evaluation index parameters, and performing quality evaluation on the thickness of the coating surface by using the thickness initial evaluation interval; evaluating the deviation value of the thickness of the coating surface according to the coating thickness measuring parameter and the thickness initial evaluation interval, and generating deviation value information according to the evaluation result of the deviation value of the thickness and the position information corresponding to the deviation value; and performing grouping fitting of the nearest position distance on the positive thickness deviation value and the negative thickness deviation value in the thickness deviation value information to obtain a grouping fitting result, obtaining the thickness uniformity of different positions of the coating surface from the grouping fitting result, and generating a coating thickness evaluation parameter according to the grouping fitting result and the preset evaluation index parameter. In the embodiment, the thickness deviation value information of different positions is obtained, information fitting is carried out according to the thickness deviation value information and the position distance information, the coating thickness evaluation parameters are further obtained according to the fitting result and the preset evaluation index parameters, and a data basis is provided for subsequent coating quality evaluation.
Further, in step S430, the method further includes:
s431: obtaining a thickness deviation value and a position distance parameter of each group of fitting results according to the grouping fitting results;
s432: performing thickness uniformity evaluation according to the thickness deviation value and the position distance parameter to obtain a thickness uniformity evaluation result;
s433: and generating the coating thickness evaluation parameter according to the difference value of the thickness uniformity evaluation result and the preset evaluation index parameter.
Specifically, the thickness deviation value information and the position distance corresponding to different positions can be used for representing the thickness uniformity of the coating surface, and the thickness deviation value and the position distance parameter of each group of fitting results are obtained from the grouped fitting results; performing thickness uniformity evaluation according to the thickness deviation value and the position distance parameter to obtain a thickness uniformity evaluation result; and generating the coating thickness evaluation parameter by combining the thickness uniformity evaluation result and the difference value of the preset evaluation index parameter, and obtaining the thickness uniformity evaluation result more accurately by fitting the position distance and the thickness deviation values corresponding to different positions, so as to obtain the more abrupt and dark change default coating thickness evaluation result and provide accurate data support for the evaluation of the subsequent coating quality.
Further, in step S400, the method further includes:
detecting the target magnesium-titanium alloy at the position of the abnormal thickness extreme value according to the coating thickness measurement parameters to obtain a size detection result of the target magnesium-titanium alloy;
performing defect evaluation on the product according to the size detection result to generate a defect evaluation result;
and identifying the defects of the target magnesium-titanium alloy according to the defect evaluation result.
Furthermore, the coating thickness can be used for representing the stability of the coating process on one hand, and the defects can be determined according to the thickness on the other hand. And detecting the target magnesium-titanium alloy at the position of the thickness abnormal extreme value according to the coating thickness measurement parameters to obtain a dimension detection result of the target magnesium-titanium alloy, performing defect evaluation on the product according to the dimension detection result to generate a defect evaluation result, and performing defect identification on the target magnesium-titanium alloy according to the defect evaluation result to achieve the effect of determining and identifying a curve according to the coating thickness.
S500: the flexibility testing module is used for testing the flexibility of the coating of the target magnesium-titanium alloy, the image acquisition module is used for acquiring the image of the surface of the target magnesium-titanium alloy, and the flexibility testing image is sent to the processing module;
s600: performing feature recognition on the flexibility measurement image through the processing module, and generating a flexibility evaluation parameter according to a feature recognition result and the preset evaluation index parameter;
specifically, after the coating of the surface of the magnesium-titanium alloy is finished, the surface of the magnesium-titanium alloy is often affected by external force which deforms the magnesium-titanium alloy according to the use condition, for example, automobile products, and even thermal expansion and cold contraction caused by severe change of external temperature in the processes of transferring, assembling, debugging and transporting cause the coating to crack so as to be separated from the surface of the base material. Coating flexibility measurement is one method of evaluating the ability of a coating to resist cracking and/or peeling from the object being coated.
The flexibility testing module is used for testing the flexibility of the coating of the target magnesium-titanium alloy, the image acquisition module is used for acquiring images of the surface of the target magnesium-titanium alloy, the flexibility testing images are sent to the processing module, the processing module is used for carrying out feature recognition on the flexibility testing images, and the result of the feature recognition can comprise the bending degree of the magnesium-titanium alloy workpiece under the condition that the coating is not damaged; and comparing the characteristic identification result with the preset evaluation index parameter to generate a flexibility evaluation parameter, so as to accurately obtain the flexibility evaluation parameter of the coating process.
S700: and generating a coating quality evaluation result of the target magnesium-titanium alloy surface according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter.
Specifically, the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter which are obtained in the previous steps are combined with one another, the coating quality of the surface of the target magnesium-titanium alloy is evaluated, and a coating quality evaluation result is obtained, so that the technical problems of how to realize real-time evaluation of the coating process quality of the surface of the magnesium-titanium alloy and improve the evaluation efficiency and accuracy are solved, the real-time evaluation of the coating process quality of the surface of the magnesium-titanium alloy is achieved, the evaluation efficiency and the accuracy are improved, the feedback can be timely performed when the coating process has problems, the process is adjusted, and the technical effect of reducing the economic loss is achieved.
In summary, the evaluation method for the coating process quality of the magnesium-titanium alloy surface provided by the embodiment of the application has the following technical effects:
1. according to the evaluation method for the coating process quality of the magnesium-titanium alloy surface, provided by the embodiment of the application, the coating quality evaluation system is used for collecting application scene data of a coating process, and evaluation index distribution based on big data is carried out according to the collection result to obtain preset evaluation index parameters; acquiring an image of the surface of the target magnesium-titanium alloy through the image acquisition module to obtain a surface image acquisition set, and sending the surface image acquisition set to the processing module through the image acquisition module; performing image recognition on the surface image collection set through the processing module, and generating a color deviation evaluation parameter and a defect evaluation parameter according to an image recognition result and the preset evaluation index parameter; measuring the coating thickness of the surface of the target magnesium-titanium alloy through the thickness measuring module to generate a coating thickness measuring parameter, and generating a coating thickness evaluation parameter according to the coating thickness measuring parameter and the preset evaluation index parameter; the flexibility testing module is used for testing the flexibility of the coating of the target magnesium-titanium alloy, the image acquisition module is used for acquiring the image of the surface of the target magnesium-titanium alloy, and the flexibility testing image is sent to the processing module; performing feature recognition on the flexibility determination image through the processing module, and generating a flexibility evaluation parameter according to a feature recognition result and the preset evaluation index parameter; generating a coating quality evaluation result of the target magnesium-titanium alloy surface according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter; the technical problems of how to realize real-time evaluation of the quality of the magnesium-titanium alloy surface coating process and improve the evaluation efficiency and accuracy are solved, the real-time evaluation of the quality of the magnesium-titanium alloy surface coating process is realized, the evaluation efficiency and accuracy are improved, and the feedback can be timely carried out when the coating process has problems, so that the process is adjusted, and the economic loss is reduced.
2. According to the embodiment of the application, each pixel value of the image and the comparison image in the surface image acquisition set is mapped into a CN probability vector, the difference between the two vector distributions is calculated to measure the perceived color difference between the two images, the difference value of the probability vector is obtained, and the color deviation evaluation parameter is generated through the difference value of the probability vector and the preset evaluation index parameter, so that the color deviation evaluation parameter can be accurately obtained.
3. In the embodiment of the application, the image information of the same position is collected at multiple angles, and the flatness of the target area in the images at different angles is evaluated, so that the defect evaluation parameters are obtained, and the accuracy of the defect evaluation parameters is improved.
4. According to the technical effect, the coating quality evaluation result of the surface of the target magnesium-titanium alloy is generated by utilizing the obtained color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter, the real-time evaluation of the coating process quality of the surface of the magnesium-titanium alloy is achieved, the evaluation efficiency and the evaluation accuracy are improved, the feedback can be timely carried out when the coating process has problems, the process is adjusted, and the economic loss is reduced.
Example two
Based on the same inventive concept as the method for evaluating the quality of the magnesium-titanium alloy surface coating process in the foregoing embodiment, as shown in fig. 5, the present application provides a system for evaluating the quality of the magnesium-titanium alloy surface coating process, wherein the system includes:
the coating quality evaluation system 100 is used for acquiring application scene data of a coating process and performing big data-based evaluation index distribution according to an acquisition result to obtain a preset evaluation index parameter;
the image acquisition module 200 is used for acquiring an image of the surface of the target magnesium-titanium alloy to obtain a surface image acquisition set, and sending the surface image acquisition set to the processing module through the image acquisition module;
the processing module 300 is used for carrying out image recognition on the surface image acquisition set, and generating a color deviation evaluation parameter and a defect evaluation parameter according to an image recognition result and the preset evaluation index parameter;
the thickness measuring module 400 is used for measuring the coating thickness of the surface of the target magnesium-titanium alloy through the thickness measuring module to generate a coating thickness measuring parameter, and generating a coating thickness evaluation parameter according to the coating thickness measuring parameter and the preset evaluation index parameter;
the flexibility measuring module 500 is used for testing the flexibility of the coating of the target magnesium-titanium alloy through the flexibility measuring module, acquiring an image of the surface of the target magnesium-titanium alloy through the image acquisition module, and sending a flexibility measuring image to the processing module;
the processing module 300 is used for carrying out feature recognition on the flexibility determination image and generating a flexibility evaluation parameter according to a feature recognition result and the preset evaluation index parameter;
and the quality evaluation module 600 is used for generating a coating quality evaluation result of the target magnesium-titanium alloy surface according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter.
Further, the processing module in the system is further configured to:
a) evaluating the image light source of the surface image acquisition set through the processing module to obtain a brightness evaluation result;
b) when the brightness evaluation result meets an expected threshold value, mapping the co-located pixel values of the surface image acquisition set and the comparison image into a probability vector through the processing module;
c) and generating the color deviation evaluation parameter according to the difference value of the probability vector and the preset evaluation index parameter.
Further, the processing module in the system is further configured to:
b1) when the brightness evaluation result does not meet the expected threshold value, generating a brightness influence characteristic according to the brightness evaluation result;
b2) performing brightness optimization compensation on the mapping result of the probability vector through the brightness influence characteristics, and generating the color deviation evaluation parameter according to the optimization compensation result;
b3) and generating a feedback brightness control parameter according to the brightness evaluation result, and feeding the feedback brightness control parameter back to the image acquisition module.
Further, the processing module in the system is further configured to:
c1) judging whether the difference value meets a preset difference threshold value or not, and when the difference value meets the preset difference threshold value, obtaining another evaluation image of the position corresponding to the difference value, wherein the other evaluation image is different from the surface image collection set in collection angle;
c2) carrying out flatness evaluation on the target area on the other evaluation image to obtain a flatness evaluation parameter;
c3) and when the flatness evaluation parameters are not abnormal, generating the color deviation evaluation parameters through the difference values of the probability vectors and the preset evaluation index parameters.
Further, the thickness determination module in the system is further configured to:
setting a thickness initial evaluation interval, carrying out thickness deviation value evaluation according to the thickness initial evaluation interval and the coating thickness measurement parameters, and generating thickness deviation value information according to an evaluation result, wherein the thickness deviation value information comprises position information;
performing grouping fitting of the nearest position distance on the positive thickness deviation value and the negative thickness deviation value in the thickness deviation value information to obtain a grouping fitting result;
and generating a coating thickness evaluation parameter according to the grouping fitting result and the preset evaluation index parameter.
Further, the thickness measuring module in the system is further configured to:
obtaining a thickness deviation value and a position distance parameter of each group of fitting results according to the grouping fitting results;
performing thickness uniformity evaluation according to the thickness deviation value and the position distance parameter to obtain a thickness uniformity evaluation result;
and generating the coating thickness evaluation parameter according to the thickness uniformity evaluation result and the difference value of the preset evaluation index parameter.
Further, the thickness determination module in the system is further configured to:
detecting the target magnesium-titanium alloy at the position of the abnormal thickness extreme value according to the coating thickness measurement parameters to obtain a size detection result of the target magnesium-titanium alloy;
performing defect evaluation on the product according to the size detection result to generate a defect evaluation result;
and identifying the defects of the target magnesium-titanium alloy according to the defect evaluation result.
For a specific working process of the module disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, which is not described herein again.
Those skilled in the art can make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The method for evaluating the coating process quality of the magnesium-titanium alloy surface is characterized by being applied to a coating quality evaluation system, wherein the coating quality evaluation system is in communication connection with an image acquisition module, a thickness measurement module, a flexibility measurement module and a processing module, and the method comprises the following steps:
acquiring application scene data of a coating process through the coating quality evaluation system, and performing big data-based evaluation index distribution according to an acquisition result to obtain a preset evaluation index parameter;
acquiring an image of the surface of the target magnesium-titanium alloy through the image acquisition module to obtain a surface image acquisition set, and sending the surface image acquisition set to the processing module through the image acquisition module;
performing image recognition on the surface image acquisition set through the processing module, and generating a color deviation evaluation parameter and a defect evaluation parameter according to an image recognition result and the preset evaluation index parameter;
measuring the coating thickness of the surface of the target magnesium-titanium alloy through the thickness measuring module to generate a coating thickness measuring parameter, and generating a coating thickness evaluation parameter according to the coating thickness measuring parameter and the preset evaluation index parameter;
the flexibility performance of a coating of the target magnesium-titanium alloy is tested through the flexibility testing module, the image acquisition module is used for acquiring an image of the surface of the target magnesium-titanium alloy, and the flexibility testing image is sent to the processing module;
performing feature recognition on the flexibility measurement image through the processing module, and generating a flexibility evaluation parameter according to a feature recognition result and the preset evaluation index parameter;
and generating a coating quality evaluation result of the target magnesium-titanium alloy surface according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter.
2. The method of claim 1, wherein the image recognition of the collection of surface images by the processing module further comprises:
a) evaluating the image light source of the surface image acquisition set through the processing module to obtain a brightness evaluation result;
b) when the brightness evaluation result meets an expected threshold value, mapping the co-located pixel values of the surface image acquisition set and the comparison image into a probability vector through the processing module;
c) and generating the color deviation evaluation parameter according to the difference value of the probability vector and the preset evaluation index parameter.
3. The method of claim 2, wherein the method further comprises:
b1) when the brightness evaluation result does not meet the expected threshold value, generating a brightness influence characteristic according to the brightness evaluation result; b2) performing brightness optimization compensation on the mapping result of the probability vector through the brightness influence characteristics, and generating the color deviation evaluation parameter according to the optimization compensation result; b3) and generating a feedback brightness control parameter according to the brightness evaluation result, and feeding the feedback brightness control parameter back to the image acquisition module.
4. The method of claim 2, wherein the method further comprises:
c1) judging whether the difference value meets a preset difference threshold value or not, and when the difference value meets the preset difference threshold value, obtaining another evaluation image of the position corresponding to the difference value, wherein the other evaluation image is different from the surface image collection set in collection angle; c2) carrying out flatness evaluation on the other evaluation image in a target area to obtain flatness evaluation parameters; c3) and when the flatness evaluation parameters are not abnormal, generating the color deviation evaluation parameters through the difference values of the probability vectors and the preset evaluation index parameters.
5. The method of claim 1, wherein the method further comprises:
setting a thickness initial evaluation interval, carrying out thickness deviation value evaluation according to the thickness initial evaluation interval and the coating thickness measurement parameters, and generating thickness deviation value information according to an evaluation result, wherein the thickness deviation value information comprises position information;
performing grouping fitting of the nearest position distance on the positive thickness deviation value and the negative thickness deviation value in the thickness deviation value information to obtain a grouping fitting result;
and generating a coating thickness evaluation parameter according to the grouping fitting result and the preset evaluation index parameter.
6. The method of claim 5, wherein the method further comprises:
obtaining a thickness deviation value and a position distance parameter of each group of fitting results according to the grouping fitting results;
performing thickness uniformity evaluation according to the thickness deviation value and the position distance parameter to obtain a thickness uniformity evaluation result;
and generating the coating thickness evaluation parameter according to the thickness uniformity evaluation result and the difference value of the preset evaluation index parameter.
7. The method of claim 1, wherein the method further comprises:
detecting the target magnesium-titanium alloy at the position of the abnormal thickness extreme value according to the coating thickness measurement parameters to obtain a size detection result of the target magnesium-titanium alloy;
performing defect evaluation on the product according to the size detection result to generate a defect evaluation result;
and identifying the defects of the target magnesium-titanium alloy according to the defect evaluation result.
8. An evaluation system for magnesium titanium alloy surface coating process quality is characterized by comprising the following components:
the coating quality evaluation system is used for acquiring application scene data of a coating process and performing big data-based evaluation index distribution according to an acquisition result to obtain a preset evaluation index parameter;
the image acquisition module is used for acquiring an image of the surface of the target magnesium-titanium alloy to obtain a surface image acquisition set, and the surface image acquisition set is sent to the processing module through the image acquisition module;
the processing module is used for carrying out image recognition on the surface image acquisition set and generating a color deviation evaluation parameter and a defect evaluation parameter according to an image recognition result and the preset evaluation index parameter;
the thickness measuring module is used for measuring the coating thickness of the surface of the target magnesium-titanium alloy through the thickness measuring module to generate a coating thickness measuring parameter, and a coating thickness evaluation parameter is generated according to the coating thickness measuring parameter and the preset evaluation index parameter;
the flexibility measuring module is used for testing the flexibility of a coating of the target magnesium-titanium alloy, acquiring an image of the surface of the target magnesium-titanium alloy through the image acquisition module and sending a flexibility measuring image to the processing module;
performing feature recognition on the flexibility determination image through the processing module, and generating a flexibility evaluation parameter according to a feature recognition result and the preset evaluation index parameter;
and the quality evaluation module is used for generating a coating quality evaluation result of the target magnesium-titanium alloy surface according to the color deviation evaluation parameter, the defect evaluation parameter, the coating thickness evaluation parameter and the flexibility evaluation parameter.
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