CN118310585A

CN118310585A - Equipment surface metal plating detection method and detection device thereof

Info

Publication number: CN118310585A
Application number: CN202410741754.6A
Authority: CN
Inventors: 张树卿; 张海涛
Original assignee: Taiyuan University of Technology
Current assignee: Taiyuan University of Technology
Priority date: 2024-06-11
Filing date: 2024-06-11
Publication date: 2024-07-09

Abstract

The invention belongs to the technical field of metal plating detection, and aims to solve the problems of poor comprehensiveness and accuracy of detection results caused by detection and evaluation of single metal plating detection indexes in the conventional method. The utility model provides a device surface metal plating detection method and a detection device thereof, comprising the following steps: obtaining a target device; performing coating thickness detection characteristic analysis according to coating thickness detection equipment and a thickness detection characteristic analysis function to generate a coating thickness detection characteristic result; performing coating component detection characteristic analysis according to the coating component detection characteristic channel to generate a coating component detection characteristic result; performing coating smoothness detection characteristic analysis according to the coating smoothness detection characteristic channel to generate a coating smoothness detection characteristic result; performing coating appearance detection feature analysis according to the coating appearance detection feature channel to obtain coating appearance detection feature results; and integrating detection characteristic results. The invention can comprehensively detect and evaluate the key quality index in the metal plating process.

Description

Equipment surface metal plating detection method and detection device thereof

Technical Field

The invention belongs to the technical field of metal plating detection, and particularly relates to a device surface metal plating detection method and a device thereof.

Background

Surface treatment of equipment is one of the common process steps, wherein the surface hardness, corrosion resistance and wear resistance of the equipment can be improved by metal plating, while also improving the appearance and texture thereof. In the metal plating process, the plating quality is required to be high, so that an effective detection method is required to ensure that the plating quality reaches the standard. However, in the conventional method for detecting the surface metal plating of the equipment, detection and evaluation are generally performed only on a single metal plating detection index, which results in poor comprehensiveness and accuracy of detection results.

Disclosure of Invention

The invention provides a device surface metal plating detection method and a detection device thereof for solving at least one technical problem in the prior art.

The invention is realized by adopting the following technical scheme: the method for detecting the metal plating on the surface of the equipment comprises the following steps:

obtaining target equipment according to the equipment metal plating end, wherein the target equipment is equipment for finishing surface metal plating;

Performing coating thickness detection feature analysis on the target equipment according to the coating thickness detection equipment and the thickness detection feature analysis function to generate a coating thickness detection feature result;

Performing coating component detection characteristic analysis on the target equipment according to the coating component detection characteristic channel to generate a coating component detection characteristic result;

performing coating smoothness detection characteristic analysis on the target equipment according to the coating smoothness detection characteristic channel to generate a coating smoothness detection characteristic result;

performing coating appearance detection feature analysis on the target equipment according to the coating appearance detection feature channel to obtain coating appearance detection feature results;

And integrating the coating thickness detection characteristic result, the coating composition detection characteristic result, the coating smoothness detection characteristic result and the coating appearance detection characteristic result, and drawing a coating detection radar chart of target equipment.

Preferably, the plating thickness detection feature analysis is performed on the target device according to the plating thickness detection device and the thickness detection feature analysis function, and the generating of the plating thickness detection feature result includes:

performing thickness detection on a plurality of positions of the target equipment according to the plating thickness detection equipment to obtain a plurality of thickness detection data sequences;

performing confidence characteristic calculation according to the thickness detection data sequences to generate a confidence thickness detection data sequence, wherein the confidence characteristic calculation comprises standardization processing and average value calculation;

loading a predetermined plating thickness characteristic data sequence of the target device;

Performing deviation calculation on the confidence thickness detection data sequence according to the preset plating thickness characteristic data sequence to obtain a plating thickness deviation characteristic data sequence;

Calculating a thickness detection characteristic evaluation index according to the plating thickness deviation characteristic data sequence and the thickness detection characteristic analysis function;

And adding the confidence thickness detection data sequence and the thickness detection characteristic evaluation index to the plating thickness detection characteristic result.

Preferably, calculating a thickness detection feature evaluation index from the plating thickness deviation feature data sequence and the thickness detection feature analysis function includes:

Calculating the average value of the plating thickness deviation characteristic data sequence to generate a plating thickness deviation center coefficient;

Performing standard deviation calculation according to the plating thickness deviation characteristic data sequence to generate a plating thickness deviation discrete coefficient;

Inputting the plating thickness deviation center coefficient and the plating thickness deviation discrete coefficient into the thickness detection characteristic analysis function to generate the thickness detection characteristic evaluation index, wherein the thickness detection characteristic analysis function is as follows:

；

Wherein TDCEI characterizes the thickness detection feature evaluation index, FCEI characterizes a thickness detection feature evaluation factor coefficient, CTD characterizes the plating thickness deviation center coefficient, TDW characterizes a preset plating thickness deviation center weight coefficient, CTH characterizes the plating thickness deviation discrete coefficient, THW characterizes a preset plating thickness deviation discrete weight coefficient, and the sum of the preset plating thickness deviation center weight coefficient and the preset plating thickness deviation discrete weight coefficient is 1, Is a natural constant.

Preferably, the plating component detection feature analysis is performed on the target device according to the plating component detection feature channel, and a plating component detection feature result is generated, including:

the plating component detection characteristic channel comprises plating component detection equipment, a plating component compliance evaluation branch and a plating component quality calculation branch;

Performing confidence coating component detection on the target equipment according to the coating component detection equipment to obtain coating component detection confidence data;

Loading predetermined coating composition characteristic data of the target equipment;

inputting the coating component detection confidence data and the preset coating component characteristic data into the coating component compliance evaluation branch to obtain a coating component compliance evaluation coefficient;

inputting the coating component compliance evaluation coefficient into the coating component quality calculation branch to generate a coating component quality coefficient, wherein the coating component quality calculation branch comprises a coating component quality calculation function, and the coating component quality calculation function is as follows:

；

Wherein CCQ represents the quality coefficient of the coating component, AQC represents the evaluation precision parameter of the coating component compliance evaluation branch, and CCE represents the coating component compliance evaluation coefficient;

And adding the coating component detection confidence data and the coating component compliance evaluation coefficient to the coating component detection characteristic result.

Preferably, the plating smoothness detection feature analysis is performed on the target device according to the plating smoothness detection feature channel, and a plating smoothness detection feature result is generated, including:

The plating smoothness detection characteristic channel comprises plating roughness detection equipment and a smoothness detection characteristic analysis function;

performing confidence coating roughness detection on the target equipment according to the coating roughness detection equipment to obtain a confidence coating roughness detection data sequence;

Loading a preset plating roughness characteristic data sequence of the target equipment, and performing deviation calculation on the confidence plating roughness detection data sequence according to the preset plating roughness characteristic data sequence to obtain a plating roughness deviation characteristic data sequence;

performing mean value calculation and standard deviation calculation according to the plating roughness deviation characteristic data sequence to generate a roughness deviation center coefficient and a roughness deviation discrete coefficient;

Inputting the roughness deviation center coefficient and the roughness deviation discrete coefficient into the smooth detection characteristic analysis function to obtain a smooth detection characteristic evaluation index, and adding the confidence coating roughness detection data sequence and the smooth detection characteristic evaluation index to the coating smooth detection characteristic result;

Wherein the smoothing detection feature analysis function is:

；

Wherein SDFEI represents the smoothness detection feature evaluation index, SDEI represents a smoothness detection feature evaluation factor coefficient, IDW represents a preset roughness deviation center weight coefficient, EID represents the roughness deviation center weight coefficient, IHW represents a preset roughness deviation discrete weight coefficient, EIH represents the roughness deviation discrete weight coefficient, and the sum of the preset roughness deviation center weight coefficient and the preset roughness deviation discrete weight coefficient is 1.

Preferably, the plating appearance detection feature analysis is performed on the target device according to the plating appearance detection feature channel, so as to obtain plating appearance detection feature results, including:

the plating appearance detection characteristic channel comprises plating image acquisition equipment, an acquisition enhancement branch and a plating appearance characteristic analysis branch;

According to the plating layer image acquisition equipment, plating layer image data of the target equipment are obtained;

performing enhancement processing on the coating image data according to the acquisition enhancement branches to generate an enhanced coating image;

And carrying out coating appearance characteristic analysis on the enhanced coating image according to the coating appearance detection characteristic analysis branch to generate a coating appearance detection characteristic result.

Preferably, the enhancement processing is performed on the plating layer image data according to the acquisition enhancement branch, so as to generate an enhanced plating layer image, which comprises the following steps:

dividing the coating image data according to a preset image anchor frame to obtain a plurality of anchor frame coating images;

Calculating signal to noise ratios according to the anchor frame plating images to obtain a plurality of anchor frame image signal to noise ratios;

Performing gain configuration on the anchor frame plating images according to a gain configuration knowledge base in the acquisition enhancement branch based on the anchor frame image signal-to-noise ratios to generate a plurality of gain configuration results, wherein each gain configuration result comprises a brightness gain characteristic coefficient and a detail gain characteristic coefficient;

Based on the gain configuration results, respectively carrying out enhancement processing on the anchor frame plating layer images according to the acquisition enhancement branches to obtain a plurality of enhancement anchor frame plating layer images, wherein the acquisition enhancement branches comprise self-adaptive enhancement processing functions, and the self-adaptive enhancement processing functions are as follows:

；

wherein EIAFC (x, y) characterizes the enhanced anchor frame plating image, CCLG characterizes the brightness gain characteristic coefficient, LFI (x, y) characterizes the image brightness layer of the anchor frame plating image, DCLG characterizes the detail gain characteristic coefficient, and DFI (x, y) characterizes the image detail layer of the anchor frame plating image;

and carrying out Laplacian pyramid fusion on the plurality of reinforced anchor frame coating images to generate the reinforced coating images.

The invention also provides a device for detecting the metal plating on the surface of the equipment, which is used for implementing the method for detecting the metal plating on the surface of the equipment and comprises the following steps:

the target equipment acquisition module is used for acquiring target equipment according to the equipment metal plating end, wherein the target equipment is equipment for completing surface metal plating;

The thickness characteristic analysis module is used for carrying out coating thickness detection characteristic analysis on the target equipment according to coating thickness detection equipment and a thickness detection characteristic analysis function to generate a coating thickness detection characteristic result;

the component characteristic analysis module is used for carrying out coating component detection characteristic analysis on the target equipment according to the coating component detection characteristic channel to generate a coating component detection characteristic result;

the smooth characteristic analysis module is used for carrying out coating smooth detection characteristic analysis on the target equipment according to the coating smooth detection characteristic channel to generate a coating smooth detection characteristic result;

the appearance characteristic analysis module is used for carrying out coating appearance detection characteristic analysis on the target equipment according to the coating appearance detection characteristic channel to obtain a coating appearance detection characteristic result;

And the radar image drawing module is used for integrating the coating thickness detection characteristic result, the coating composition detection characteristic result, the coating smoothness detection characteristic result and the coating appearance detection characteristic result and drawing a coating detection radar image of target equipment.

Compared with the prior art, the invention has the beneficial effects that:

according to the invention, by using the plating thickness detection equipment and the thickness detection characteristic analysis function, the plating thickness detection characteristic analysis is carried out on the target equipment, so that a plating thickness detection characteristic result is generated, the non-uniformity of the plating thickness can be detected in time, and the accuracy of metal plating detection is improved; the plating component detection characteristic channel is utilized to carry out plating component detection characteristic analysis on target equipment, a plating component detection characteristic result is generated, the condition that plating components do not meet the requirements can be detected in time, and the accuracy of metal plating detection is improved; the plating smoothness detection characteristic channel is used for carrying out plating smoothness detection characteristic analysis on the target equipment to generate a plating smoothness detection characteristic result, so that the problem that the surface smoothness of the plating layer does not reach the standard can be found in time, and the accuracy of metal plating detection is improved; and carrying out coating appearance detection feature analysis on the target equipment by using the coating appearance detection feature channel to obtain coating appearance detection feature results, so that appearance defects of the coating can be rapidly and accurately detected, and the accuracy of metal coating detection is improved. By the method, key quality indexes in the metal plating process can be comprehensively detected and evaluated, and the accuracy and the comprehensiveness of detection are realized, so that the quality stability of the metal plating on the surface of equipment is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a method for detecting metal plating on a surface of an apparatus according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a device for detecting metal plating on a surface of an apparatus according to an embodiment of the present invention;

fig. 3 is an internal structural diagram of a computer device according to an embodiment of the present invention.

Detailed Description

Technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the examples of this invention without making any inventive effort, are intended to fall within the scope of this invention.

It should be understood that the structures, proportions, sizes, etc. shown in the drawings are merely for the purpose of understanding and reading the disclosure, and are not intended to limit the scope of the invention, which is defined by the appended claims, and any structural modifications, proportional changes, or dimensional adjustments, which may be made by those skilled in the art, should fall within the scope of the present disclosure without affecting the efficacy or the achievement of the present invention, and it should be noted that, in the present disclosure, relational terms such as first and second are used solely to distinguish one entity from another entity without necessarily requiring or implying any actual relationship or order between such entities.

As shown in fig. 1, the embodiment of the application provides a method for detecting metal plating on a surface of equipment, which comprises the following steps:

The metal plating end of the equipment is a management interface of a metal plating management system or a part of the system for interacting with a user; the equipment metal plating end provides all equipment information, and any equipment with finished surface metal plating is extracted from the equipment information to serve as target equipment, namely an object needing metal plating detection; metal plating is the process of depositing, coating, or otherwise covering metal on the surfaces of equipment to increase its corrosion resistance, electrical conductivity, appearance, etc.

Preparing a plating thickness detection device suitable for a target device, including a laser scanner, an ultrasonic sensor, an X-ray diffractometer, etc., specifically selecting a plating thickness detection device to be placed at a proper position of the target device depending on the characteristics of the target device and the required detection accuracy, starting the detection device to detect, scanning the surface of the target device by the detection device to obtain plating thickness data, performing feature analysis on the plating thickness data, and calculating a feature analysis result using the thickness detection feature analysis function to generate the plating thickness detection feature result, wherein specific feature analysis and feature calculation methods are described in detail in the following steps, and only briefly mentioned herein.

Further, performing a coating thickness detection feature analysis on the target device according to a coating thickness detection device and a thickness detection feature analysis function, generating a coating thickness detection feature result, including:

A plurality of locations for thickness detection at the target device surface are determined, the locations being at different locations of the device to ensure representative detection of the entire plated surface. And detecting the thickness of each selected position through plating thickness detection equipment, specifically, detecting each position once according to the sequence to obtain thickness data of the surface plating layer, after traversing all positions, forming a thickness detection data sequence according to the sequence of the thickness data of each position, and detecting for a plurality of times to obtain a plurality of thickness detection data sequences.

And carrying out confidence characteristic calculation on the obtained multiple thickness detection data sequences, wherein the confidence characteristic calculation comprises standardization processing and average calculation, specifically, the multiple thickness detection data sequences are subjected to standardization processing to ensure that the data are in the same order of magnitude, the standardization is to process the data according to the columns of the feature matrix, the data standardization method comprises an extremum method, a standard deviation method, a three-fold line method and the like, the most commonly used method is Z-Score standardization, the method is used for carrying out data standardization on the average value and the standard deviation of the original data, and the processed data conform to standard normal distribution, namely, the average value is 0 and the standard deviation is 1.

And carrying out average calculation on the normalized multiple thickness detection data sequences, namely extracting and summing the data corresponding to each position in the sequence, dividing the data by the data quantity to obtain the average value of each position, and arranging the average value of each position according to the original sequence to obtain a confidence thickness detection data sequence, wherein each data point of the sequence represents the average confidence thickness value of the corresponding position and reflects the average thickness condition of the coating at the position.

A predetermined sequence of plating thickness characteristic data for the target device is obtained from a design document for the target device, the sequence being the expected plating thickness values for different locations, the locations also being arranged in a prior order for comparison.

Matching the expected thickness value of each position in the predetermined plating thickness characteristic data sequence with the actual thickness value of the corresponding position in the confidence thickness detection data sequence, calculating the deviation between the actual thickness value and the expected thickness value for each matched position, subtracting the expected value from the actual value, and combining the calculated deviation values into a plating thickness deviation characteristic data sequence, wherein each deviation value corresponds to one position in the confidence thickness detection data sequence and reflects the difference between the actual thickness and the expected thickness at the position.

The sequence of plated thickness deviation feature data is calculated using a thickness detection feature analysis function to obtain a thickness detection feature evaluation index, which provides a reference for evaluating the quality of the plated layer, and the specific analysis function and calculation method are detailed in the subsequent steps, which are only briefly mentioned here.

And integrating the confidence thickness detection data sequence and the thickness detection characteristic evaluation index to obtain a coating thickness detection characteristic result so as to reflect the actual thickness condition of each position.

Further, calculating a thickness detection feature evaluation index from the plating thickness deviation feature data sequence and the thickness detection feature analysis function, comprising:

；

And calculating the average value of the plating thickness deviation characteristic data sequence, namely adding all data in the sequence, dividing the added value by the number of data points to obtain the average value of the sequence, and taking the calculated average value as a plating thickness deviation center coefficient which represents the center position, namely the average deviation level, of the plating thickness deviation characteristic data sequence.

Calculating standard deviation of the plating thickness deviation characteristic data sequence, wherein the standard deviation is a measure of the average distance of the data deviation from the mean value, and the calculation formula is as follows:

;

wherein, The standard deviation of the data sequence is calculated,Indicating that the i-th data point is present,Represents the mean of the data sequence and n represents the total number of data points.

Dividing the standard deviation by the mean value of the data sequence to obtain a plating thickness deviation discrete coefficient, wherein the coefficient represents the degree of the dispersion of the data relative to the mean value of the data, and is used for evaluating the degree of the dispersion of the plating thickness deviation data, namely the degree of fluctuation of the data near the mean value.

The thickness detection feature analysis function is as follows:

；

The TDW and the THW respectively represent a preset plating thickness deviation center weight coefficient and a preset plating thickness deviation discrete weight coefficient, and the weight coefficients are set according to actual requirements and are used for adjusting the contribution proportion of each parameter in the evaluation factor coefficient, so that the evaluation result better accords with the actual requirements and preferences.

Firstly, adopting a second formula, and combining a plating thickness deviation central coefficient CTD and a plating thickness deviation discrete coefficient CTH based on a weight value to evaluate the thickness detection characteristic to obtain a thickness detection characteristic evaluation factor coefficient FCEI; and substituting the evaluation factor coefficient FCEI into a first formula, and carrying out indexing processing on the evaluation factor coefficient through a natural index function so as to obtain a thickness detection characteristic evaluation index, wherein the indexing processing can map the evaluation factor coefficient into a wider range, so that an evaluation result is clearer and more visual, and the larger the index is, the higher the evaluation of the thickness detection characteristic is.

Plating component detection feature channels include plating component detection devices such as spectrometers, mass spectrometers, chemical analysis instruments, etc., depending on the desired detection accuracy and characteristics of the target component. And placing the target equipment at a proper position of the plating component detection equipment, and starting the detection equipment to detect so as to acquire plating component data. The coating composition data is analyzed through a coating composition compliance evaluation branch and a coating composition quality calculation branch of the coating composition detection characteristic channel to obtain coating composition detection characteristic results, and a specific characteristic analysis method is described in detail in the subsequent steps, and is only briefly mentioned herein.

Further, performing coating composition detection feature analysis on the target device according to the coating composition detection feature channel to generate a coating composition detection feature result, including:

；

The plating component detection characteristic channel comprises plating component detection equipment, a plating component compliance evaluation branch and a plating component mass calculation branch, wherein the plating component detection equipment is used for detecting plating components and comprises a spectrometer, a mass spectrometer, a chemical analysis instrument and the like; the coating component compliance evaluation branch is a pre-trained twin neural network model and comprises two neural networks with the same structure and shared parameters, and the two neural networks are used for identifying the similarity of detection data and standard data so as to perform coating component compliance evaluation; the coating composition quality calculation branch comprises a coating composition quality calculation function for calculating a coating composition quality coefficient.

The plating component detection equipment is used for detecting the target equipment for a plurality of times, and average value processing is carried out on plating component data obtained by each detection, so that the purpose of the method is to eliminate random errors possibly existing, reduce measurement errors and finally obtain plating component detection confidence data.

The predetermined coating composition characteristic data of the target device is loaded, which may be from industry standards, manufacturer specifications, or known experimental data, as standard data for assessing coating composition compliance of the target device.

And inputting the coating component detection confidence data and the preset coating component characteristic data into a coating component compliance evaluation branch, and performing compliance evaluation on the coating component compliance evaluation branch, namely comparing the detected coating component data with the preset component characteristic data, calculating the similarity, and outputting a coating component compliance evaluation coefficient according to a similarity calculation result, wherein the larger the coefficient is, the more the coating component of the target equipment meets the expected requirement.

And taking the coating component compliance evaluation coefficient as input data, inputting the input data into a coating component quality calculation branch, wherein the calculation branch comprises a coating component quality calculation function which adopts a logarithmic function form, and calculating the coating component quality coefficient according to the coating component compliance evaluation coefficient and an evaluation precision parameter, wherein the quality coefficient is used for representing the coating component quality level of target equipment.

The coating composition mass calculation function is as follows:

；

wherein, the quality coefficient CCQ of the coating composition represents the quality level of the coating composition of the target equipment, and the higher the value is, the better the quality of the coating composition is; AQC is an evaluation precision parameter of a coating component compliance evaluation branch, namely the accuracy degree or precision of the compliance evaluation, and the parameter is used for adjusting the weight or influence degree of the compliance evaluation; CCE represents a coating composition compliance evaluation coefficient of the target device.

The quality coefficient of the coating component is calculated by multiplying the compliance evaluation coefficient and the evaluation precision parameter and taking the natural logarithm of the result, and the quality level of the coating component of the target equipment can be reflected more accurately by the method.

And integrating the coating component detection confidence data and the coating component compliance evaluation coefficient to obtain a coating component detection characteristic result.

The plating smoothness detection feature path includes plating smoothness detection devices such as surface roughness meters, scanning electron microscopes, etc., with the specific choice depending on the detection accuracy desired and the nature of the surface features. And placing the target equipment at a proper position of the plating smoothness detection equipment, and starting the detection equipment to detect so as to acquire plating smoothness data. The plating smoothness data is analyzed using a smoothness detection feature analysis function of the plating smoothness detection feature channel to obtain plating smoothness detection feature results, and specific analysis methods are described in detail in subsequent steps, which are only briefly mentioned herein.

Further, performing coating smoothness detection feature analysis on the target device according to the coating smoothness detection feature channel to generate a coating smoothness detection feature result, including:

Wherein the smoothing detection feature analysis function is:

；

The plating smoothness detection characteristic channel comprises plating roughness detection equipment and a smoothness detection characteristic analysis function, wherein the plating roughness detection equipment is used for detecting the plating roughness of target equipment and comprises a surface roughness measuring instrument, a scanning electron microscope and the like; the smooth detection characteristic analysis function is used for carrying out smooth detection characteristic analysis, and a smooth detection characteristic evaluation index is obtained through calculation.

The method for acquiring the confidence coating roughness detection data sequence is similar to the confidence thickness detection data sequence, specifically, the plating roughness detection equipment is used for detecting a plurality of positions of the target equipment for multiple times, each position is traversed in sequence by each detection, the roughness data of each position form the roughness detection data sequence, and the roughness detection data sequences are obtained through multiple times of detection. And then, carrying out standardization processing on the plurality of roughness detection data sequences, and carrying out mean value calculation on the plurality of roughness data corresponding to each position on the plurality of roughness detection data sequences subjected to the standardization processing, wherein the mean value calculation results form a confidence plating roughness detection data sequence.

And acquiring a preset plating roughness characteristic data sequence through a design document of the target equipment, wherein each data point in the sequence corresponds to the position of the corresponding data point of the confidence plating roughness detection data sequence and is used as standard data to be compared with the detection data.

And performing deviation calculation on the preset plating roughness characteristic data sequence and the confidence plating roughness detection data sequence, namely performing difference calculation on preset data corresponding to each position and detection data to obtain a deviation value of the position, wherein the deviation values of all the positions form the plating roughness deviation characteristic data sequence.

Average value calculation is carried out on the plating roughness deviation characteristic data sequence, namely all data in the sequence are added and then divided by the data quantity, and a roughness deviation center coefficient is obtained; and carrying out standard deviation calculation according to the plating roughness deviation characteristic data sequence, and dividing the standard deviation by the mean value of the data sequence to obtain a plating roughness deviation discrete coefficient, wherein the coefficient represents the degree of the dispersion of the data relative to the mean value of the data, and is used for evaluating the degree of the dispersion of the plating roughness deviation data, namely the fluctuation degree of the data near the mean value.

And taking the obtained roughness deviation center coefficient and the roughness deviation discrete coefficient as inputs, inputting the inputs into a smooth detection characteristic analysis function for calculation to obtain a smooth detection characteristic evaluation index, wherein the larger the index is, the higher the evaluation of the smooth detection characteristic is. And integrating the confidence coating roughness detection data sequence and the smooth detection characteristic evaluation index to obtain the coating smooth detection characteristic result.

The smoothing detection feature analysis function is as follows:

；

The preset roughness deviation center weight coefficient IDW and the preset roughness deviation discrete weight coefficient IHW are set according to actual requirements and are used for adjusting the contribution proportion of each parameter in the evaluation factor coefficient, so that the evaluation result is more in line with the actual requirements and preferences.

Firstly, combining a roughness deviation center coefficient EID and a roughness deviation discrete coefficient EIH based on a weight value to evaluate roughness detection characteristics to obtain a roughness detection characteristic evaluation factor coefficient SDEI; and then, carrying out indexing treatment on the evaluation factor coefficient through a natural index function, so as to obtain a roughness detection characteristic evaluation index, wherein the larger the index is, the higher the roughness detection characteristic evaluation is.

The plating appearance detection feature channel comprises plating image acquisition equipment, including a high-resolution camera, an optical detection instrument and the like, and the specific selection depends on the required detection precision and the properties of appearance features. And placing the target equipment at a proper position of the plating appearance detection equipment, and starting the detection equipment to detect so as to acquire plating appearance image data. The coating appearance image data is analyzed by using the acquisition enhancement branch and the coating appearance characteristic analysis branch of the coating appearance detection characteristic channel to obtain coating appearance detection characteristic results, and a specific analysis method is described in detail in the subsequent steps, and is only briefly mentioned herein.

Further, performing coating appearance detection feature analysis on the target device according to the coating appearance detection feature channel to obtain coating appearance detection feature results, including:

The plating appearance detection characteristic channel comprises plating layer image acquisition equipment, an acquisition enhancement branch and a plating layer appearance characteristic analysis branch, wherein the plating layer image acquisition equipment is used for carrying out image acquisition on target equipment and comprises a high-resolution camera, an optical detection instrument and the like; the collecting and enhancing branch is used for enhancing the obtained coating image, and the enhancing treatment can comprise operations of adjusting brightness, contrast, color balance and the like of the image so as to improve the quality and definition of the image, thereby better showing the appearance characteristics of the coating; the analysis branch of the appearance characteristics of the coating is a model constructed based on a neural network and is used for carrying out appearance characteristic analysis on the enhanced coating image so as to identify specific characteristics in the coating image, such as defect characteristics and the like, so as to realize defect identification and evaluation based on the image, thereby evaluating the appearance quality of the coating on the surface of the target equipment.

Therefore, the plating appearance detection characteristic channel acquires a plating image through the plating image acquisition equipment, then carries out enhancement treatment on the image through the acquisition enhancement branch, and finally carries out appearance characteristic analysis on the enhanced image through the plating appearance characteristic analysis branch so as to evaluate the appearance quality of the plating layer on the surface of the target equipment.

And acquiring an image of the target equipment by the coating image acquisition equipment to acquire coating image data.

And the acquired coating image data is subjected to enhancement processing through the acquisition enhancement branch so as to generate an enhanced coating image, and the enhancement processing aims at improving the quality, definition and usability of the image, so that the subsequent coating appearance characteristic analysis branch can analyze the appearance characteristics of the coating more accurately.

Enhancement processing involves a series of image processing techniques including, but not limited to, adjusting the brightness, contrast, and saturation of an image to enhance the overall visual effect of the image; noise and interference in the image are reduced, so that definition and quality of the image are improved; enhancing edges and details of the image to highlight features and textures of the coating; correcting the color deviation of the image to make the image more close to the true coating color.

The coating appearance detection feature analysis branch is a neural network model and is used for analyzing the coating image subjected to enhancement treatment to identify coating appearance features and defects and generate corresponding detection results. Specifically, the enhanced image is preprocessed, such as resizing, clipping, normalizing, etc., so as to be suitable for input of a neural network, features in the image are extracted using a neural network model, the features may represent various appearance features of the plating layer, such as texture, color, shape, etc., the extracted features are input into the model, the model analyzes the features and identifies defects or abnormal features present in the plating layer, and plating appearance detection feature results including information of positions, types, severity, etc., of the defects are generated according to the output of the model.

The training process of the coating appearance detection characteristic analysis branch is as follows:

A representative coating image dataset is first collected, including coating images of different types, different qualities, and under different conditions, which cover various possible defects, surface features, and appearance variations. The collected image data is preprocessed, including resizing, cropping, normalization, enhancement, etc., to ensure quality and consistency of the image data and to adapt it to the input of the neural network.

The method comprises the steps of selecting a neural network to construct a network structure of a coating appearance detection feature analysis branch, training a neural network model by using a prepared coating image data set, continuously adjusting network parameters through a back propagation algorithm in the training process, enabling the network parameters to learn features in coating images, accurately identifying appearance features and defects of the coating, evaluating and optimizing the model after the training is finished so as to ensure generalization capability of the model on unseen data, evaluating the model by using a verification set, adjusting super parameters, network structures and the like, and improving the performance of the model.

After evaluation and tuning, a coating appearance detection feature analysis branch is obtained and is used for carrying out appearance feature analysis and defect identification on a coating image, and in practical application, the model receives the coating image through an image input interface and outputs a corresponding detection result.

Further, the enhancement processing is performed on the coating image data according to the acquisition enhancement branch, so as to generate an enhanced coating image, which comprises the following steps:

；

The preset image anchor frame is a predefined frame for defining a region of interest in the image, and in the plated image data, if analysis of a specific region is desired, the image may be divided and a portion of interest obtained using the preset image anchor frame.

Specifically, the size and proportion of the anchor frame of the preset image are determined first, the size and proportion can be selected according to the requirements of specific application and the characteristics of the coating image, and a plurality of anchor frames with different sizes and proportions can be used for covering targets with different sizes and proportions. According to the preset size and proportion, a series of anchor frames are generated on the coating image, and the anchor frames can be arranged at different positions of the image so as to cover the whole image. Cutting out corresponding areas on the coating images to serve as anchor frame coating images according to the positions of the generated anchor frames, repeating cutting steps aiming at each preset image anchor frame in the coating images until all the interested areas are divided and corresponding anchor frame coating images are generated, and obtaining a plurality of anchor frame coating images.

The signal-to-noise ratio is an indicator of the relative strength between the signal and noise used to evaluate the sharpness of the signal and the degree of noise in an image, and in image processing, the calculation of the signal-to-noise ratio is used to determine the reliability of the image quality. Specifically, a signal region and a noise region in each anchor frame plating image are first determined, the signal region referring to a portion containing target information, and the noise region referring to a background or other interference portion in the image. Calculating an average value of pixel values for the signal region of each anchor frame plating image, the average value representing the intensity of the signal as a signal average value, and calculating an average value of squares of deviations from the signal average value of the pixel values of the signal region, the value representing the degree of variation of the signal as a signal variance; for each noise region of the anchor frame plating image, an average value of pixel values is calculated, which represents the intensity of noise as a noise average value, and for the pixel value of the noise region, an average value of squares of deviation thereof from the noise average value, which represents the degree of variation of noise as a noise variance, is calculated. Dividing the signal variance by the noise variance yields a signal-to-noise ratio, the higher the signal-to-noise ratio, the greater the strength of the signal relative to noise, and the better the image quality. Based on the signal-to-noise ratio calculation is carried out on the plurality of anchor frame plating images, and a plurality of anchor frame image signal-to-noise ratios are obtained.

According to the signal-to-noise ratios of the multiple anchor frame images and the gain configuration knowledge base in the acquisition enhancement branch, carrying out gain configuration on each anchor frame plating layer image, wherein the gain configuration aims to adjust the brightness and detail of the image according to the image quality and content characteristics so as to improve the visual effect and recognition performance of the image.

In particular, determining the appropriate gain level based on the magnitude of the signal-to-noise ratio, an image with a high signal-to-noise ratio generally implies a higher signal quality and therefore requires less gain; while images with low signal-to-noise ratios require more gain to intensify the signal. And establishing a gain configuration knowledge base, wherein the gain configuration knowledge base comprises brightness gain and detail gain setting values under different signal-to-noise ratio ranges, and selecting corresponding gain parameters from the knowledge base according to the signal-to-noise ratio. Including a luminance gain parameter and a detail gain parameter. The selected brightness gain parameters and detail gain parameters are applied to the corresponding anchor frame plating image for gain configuration, which can be achieved by adjusting pixel values of the image, including adjusting brightness, adjusting details of the image, thereby improving visual quality and recognizability of the image. For each anchor frame plating image, a corresponding gain configuration result is generated, including the applied luminance gain feature coefficients and detail gain feature coefficients.

Based on a plurality of gain configuration results, respectively carrying out enhancement processing on a plurality of anchor frame coating images according to an adaptive enhancement processing function in an acquisition enhancement branch to obtain a plurality of enhancement anchor frame coating images, wherein the adaptive enhancement processing function is used for carrying out enhancement processing on the images according to an image brightness layer and an image detail layer, a brightness gain characteristic coefficient and a detail gain characteristic coefficient of each anchor frame coating image, and the functions are as follows:

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Wherein EIAFC (x, y) represents the pixel value of the enhanced anchor frame plating image, wherein (x, y) is the pixel coordinate in the image, CCLG represents the brightness gain characteristic coefficient for adjusting the brightness of the image, LFI (x, y) represents the image brightness layer of the anchor frame plating image, is the brightness value of each pixel in the image, DCLG represents the detail gain characteristic coefficient for adjusting the details of the image, DFI (x, y) represents the image detail layer of the anchor frame plating image, is the detail value of each pixel in the image.

The function is to multiply the brightness layer and detail layer of the image with corresponding gain coefficients respectively, then add them to get the enhanced image pixel value, the brightness gain characteristic coefficient and detail gain characteristic coefficient are determined by the gain configuration process, the gain process can be self-adaptively adjusted according to the brightness and detail condition of the image to keep the natural appearance of the image and enhance the detail information of the image.

Laplacian pyramid fusion is an image fusion technique for merging multiple reinforcement anchor frame plating images while preserving their details to produce a final reinforcement plating image. Specifically, firstly, carrying out Gaussian pyramid decomposition on each reinforced anchor frame coating image, wherein the Gaussian pyramid is a reduced version of a series of images, each layer is half of the previous layer, carrying out Laplacian pyramid construction on each Gaussian pyramid, wherein the Laplacian pyramid consists of the difference value between each layer of the Gaussian pyramid and the image sampled on the next layer, carrying out weighted fusion on each image of the corresponding layer, carrying out weighted average on the fusion, and carrying out weight adjustment according to application requirements, and reconstructing the fused Laplacian pyramid to obtain the final reinforced coating image.

Through the process, a plurality of reinforced anchor frame coating images can be fused into a reinforced coating image with better quality and more details, so that the accuracy and the reliability of coating appearance detection are improved.

And integrating the coating thickness detection characteristic result, the coating composition detection characteristic result, the coating smoothness detection characteristic result and the coating appearance detection characteristic result obtained in the previous steps into a data set, wherein each characteristic result corresponds to the same target equipment. The use of data visualization tools, such as Matplotlib libraries, plotly libraries, etc., to draw radar maps, also known as spider maps or star maps, is suitable for use in exposing a plurality of feature indices.

The integrated feature result set is mapped onto different axes of the radar map, each axis of the radar map representing a feature index and each vertex representing a feature result. And obtaining a target equipment plating detection radar chart according to the mapped data, wherein the scale on each axis can reflect the range of the characteristic results and the relative sizes among the characteristic results. By drawing the radar image for detecting the plating of the target equipment, the detection results of the plating thickness, the composition, the smoothness, the appearance and the like can be intuitively displayed, and the comprehensive evaluation of the plating quality of the target equipment is facilitated.

In summary, the method for detecting the metal plating on the surface of the equipment provided by the embodiment of the invention has the following technical effects:

1. Performing coating thickness detection feature analysis on the target equipment by using coating thickness detection equipment and a thickness detection feature analysis function to generate coating thickness detection feature results, so that non-uniformity of coating thickness can be timely detected, and accuracy of metal coating detection is improved;

2. the plating component detection characteristic channel is utilized to carry out plating component detection characteristic analysis on target equipment to generate a plating component detection characteristic result, so that the condition that plating components do not meet the requirements can be detected in time, and the accuracy of metal plating detection is improved;

3. Performing coating smoothness detection characteristic analysis on the target equipment through the coating smoothness detection characteristic channel to generate a coating smoothness detection characteristic result, so that the problem that the coating surface smoothness does not reach the standard can be found in time, and the accuracy of metal coating detection is improved;

4. And carrying out coating appearance detection feature analysis on the target equipment by using the coating appearance detection feature channel to obtain coating appearance detection feature results, so that appearance defects of the coating can be rapidly and accurately detected, and the accuracy of metal coating detection is improved.

By the method, key quality indexes in the metal plating process can be comprehensively detected and evaluated, and the accuracy and the comprehensiveness of detection are realized, so that the quality stability of the metal plating on the surface of equipment is improved.

Based on the same inventive concept as the method for detecting the metal plating on the surface of the equipment in the foregoing embodiment, as shown in fig. 2, the present application provides an apparatus for detecting the metal plating on the surface of the equipment, comprising:

Further, the device also comprises a plating thickness detection characteristic result acquisition module for executing the following operation steps:

Further, the device further comprises a thickness detection characteristic evaluation index generation module for executing the following operation steps:

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wherein TDCEI characterizes the thickness detection characteristic evaluation index, FCEI characterizes the thickness detection characteristic evaluation factor coefficient, CTD characterizes the plating thickness deviation center coefficient, TDW characterizes a preset plating thickness deviation center weight coefficient, CTH characterizes the plating thickness deviation discrete coefficient, THW characterizes a preset plating thickness deviation discrete weight coefficient, and the sum of the preset plating thickness deviation center weight coefficient and the preset plating thickness deviation discrete weight coefficient is 1.

Further, the device also comprises a plating layer composition detection characteristic result acquisition module for executing the following operation steps:

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Further, the device also comprises a plating layer smoothness detection characteristic result acquisition module for executing the following operation steps:

Wherein the smoothing detection feature analysis function is:

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Further, the device also comprises a plating appearance detection characteristic result acquisition module for executing the following operation steps:

Further, the device also comprises an enhanced coating image generation module for executing the following operation steps:

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From the foregoing detailed description of a method for detecting metal plating on a surface of an apparatus, it will be apparent to those skilled in the art that a device for detecting metal plating on a surface of an apparatus in this embodiment is described more simply because it corresponds to the method disclosed in the embodiments, and the relevant points are described in the method section.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, and a network interface connected by a device bus, wherein the processor of the computer device is configured to provide computing and control capabilities; the memory of the computer device comprises a nonvolatile storage medium, an internal memory, an external memory and a memory, wherein the nonvolatile storage medium stores an operating device, a computer program and a database, and the internal memory provides an environment for the operating device and the computer program in the nonvolatile storage medium to run; the network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a device surface metallization detection method.

It will be appreciated by those skilled in the art that the structure shown in FIG. 3 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present invention should be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. The method for detecting the metal plating on the surface of the equipment is characterized by comprising the following steps of:

2. The method for detecting the metal plating on the surface of equipment according to claim 1, wherein the method comprises the following steps: performing coating thickness detection feature analysis on the target equipment according to the coating thickness detection equipment and the thickness detection feature analysis function to generate coating thickness detection feature results, wherein the method comprises the following steps:

3. The method for detecting the metal plating on the surface of equipment according to claim 2, wherein: calculating a thickness detection feature evaluation index according to the plating thickness deviation feature data sequence and the thickness detection feature analysis function, including:

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4. The method for detecting the metal plating on the surface of equipment according to claim 1, wherein the method comprises the following steps: performing coating component detection feature analysis on the target equipment according to the coating component detection feature channel to generate a coating component detection feature result, wherein the method comprises the following steps:

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5. The method for detecting the metal plating on the surface of equipment according to claim 1, wherein the method comprises the following steps: performing coating smoothness detection feature analysis on the target equipment according to the coating smoothness detection feature channel to generate a coating smoothness detection feature result, wherein the coating smoothness detection feature result comprises:

Wherein the smoothing detection feature analysis function is:

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6. The method for detecting the metal plating on the surface of equipment according to claim 1, wherein the method comprises the following steps: performing coating appearance detection feature analysis on the target equipment according to the coating appearance detection feature channel to obtain coating appearance detection feature results, wherein the coating appearance detection feature results comprise:

7. The method for detecting the metal plating on the surface of equipment according to claim 6, wherein: performing enhancement processing on the coating image data according to the acquisition enhancement branch to generate an enhanced coating image, including:

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8. A device surface metallization detection apparatus for performing the device surface metallization detection method according to any one of claims 1-7, comprising: