CN114565556A - Method for identifying plasma electrolytic oxidation reaction state based on image processing - Google Patents
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- 239000002184 metal Substances 0.000 description 2
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 1
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 1
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- 239000002932 luster Substances 0.000 description 1
- 229910052749 magnesium Inorganic materials 0.000 description 1
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- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 229910044991 metal oxide Inorganic materials 0.000 description 1
- 150000004706 metal oxides Chemical class 0.000 description 1
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Abstract
The invention discloses a plasma electrolytic oxidation reaction state identification method based on image processing, which comprises the following steps: acquiring a spark image to be processed and obtaining the area S of the surface of a workpiece; graying the spark image to be processed; segmenting sparks and backgrounds of the grayed spark images to be processed to obtain target spark images; performing morphological processing on the target spark image to obtain spark targets separated from each other; extracting edge features of each spark target in the target spark image; and communicating the edge characteristics by adopting a connected domain method to obtain the total number of sparks, dividing the total number by the surface area S of the workpiece to obtain the number of sparks in unit area, and identifying the plasma electrolytic oxidation reaction state at the current moment according to the number of sparks in unit area. The accuracy of identifying the reaction state of the plasma electrolytic oxidation is improved, and the reaction process can be accurately adjusted.
Description
Technical Field
The invention belongs to the technical field of image processing, and relates to a plasma electrolytic oxidation reaction state identification method based on image processing.
Background
The plasma electrolytic oxidation technology is applied to the surface treatment of aluminum, magnesium, titanium and the alloy thereof. The principle is that under the action of a pulse electric field and under the condition of proper electrolyte, the surface of a metal workpiece serving as an anode generates complex chemical, electrochemical and plasma spark discharge reactions, and under the action of instantaneous high temperature and high pressure generated by discharge, a ceramic layer which takes matrix metal oxide as a main component and is supplemented with electrolyte components grows on the surface of the metal workpiece.
The plasma electrolytic oxidation reaction state is closely related to the growth process of the ceramic layer, and the micro-morphology and the protective performance of the finally grown ceramic layer are greatly influenced. The plasma electrolytic oxidation discharge reaction process can be mainly divided into 3 stages, a large amount of bubbles are generated on the surface of a workpiece which is subjected to the plasma discharge reaction at first, the metallic luster gradually disappears, sparks are gradually generated, and the local part flickers. This stage is the anodization stage. After a few minutes, a dense small spark appears on the surface of the material, which is the spark discharge phase. The spark then gradually breaks and evolves into a large, sparse spark, which is the stage of micro-arc discharge, which can also occur if ablation occurs. The specific reaction state of the plasma electrolytic oxidation reaction device is related to the number of sparks, but the scale of the discharge sparks is too small, the distribution is too dense, the characteristics of the discharge sparks are not obvious enough, the change of the number of the tiny discharge sparks observed by naked eyes can only be divided into three stages roughly, and the plasma electrolytic oxidation reaction state and the specific evolution process at a certain moment cannot be accurately identified.
Disclosure of Invention
The invention aims to provide a method for identifying the electrolytic oxidation reaction state of plasma based on image processing, which solves the problem that the number of discharge sparks cannot be accurately obtained in the prior art.
The technical scheme adopted by the invention is that the method for identifying the plasma electrolytic oxidation reaction state based on image processing comprises the following steps:
step 1, acquiring a spark image to be processed, and obtaining the surface area S of a workpiece;
step 3, segmenting sparks and backgrounds of the grayed spark images to be processed to obtain target spark images;
step 5, extracting the edge characteristics of each spark target in the target spark image;
and 6, communicating the edge characteristics by adopting a communication domain method to obtain the total number of sparks, dividing the total number by the surface area S of the workpiece to obtain the number of sparks in unit area, and identifying the plasma electrolytic oxidation reaction state at the current moment according to the number of sparks in unit area.
The invention is also characterized in that:
the specific process of the step 2 is as follows: and converting the color of a pixel point in the spark image to be processed into gray scale by a weighted average value method.
The specific process of the step 3 is as follows: firstly, taking a gray value mean value M of a grayed spark image to be processed, arbitrarily selecting a gray value T to divide a gray histogram of the image into a front part A and a rear part B, wherein the mean value of the two parts is MA and MB, the proportion of the pixel number of the part A to the total pixel number is marked as PA, the proportion of the pixel number of the part B to the total pixel number is marked as PB, calculating the maximum between-class variance and taking the maximum between-class variance as a gray threshold value T, and then dividing the image into a binary image by using the gray threshold value T to obtain a target spark image.
The specific process of the step 4 is as follows: and carrying out corrosion operation on the target spark image, and then expanding to obtain mutually separated spark targets.
The step 5 specifically comprises the following steps:
step 5.1, performing convolution on the target spark image by using a Gaussian filter to obtain a filtered pixel point e;
step 5.2, applying a pair of convolution arrays Sx,SyConvolving a window of 3x3 in the target spark image as A and Sobel operators to obtain gradient values G of pixel points e in x and y directions respectivelyXAnd GyThen calculating the gradient intensity G and the gradient direction theta of the pixel point e;
step 5.3, according to the gradient direction, carrying out non-maximum suppression on the gradient amplitude to obtain edge points;
and 5.4, processing the edge points by using high and low thresholds to obtain the edge characteristics of each spark target.
The specific process of the step 5.3 is as follows: dividing the gradient direction into E, NE, N, NW, W, SW, S and SE, wherein 0 represents 0-45 degrees, 1 represents 45-90 degrees, 2 represents-90 degrees-45 degrees, 3 represents-45 degrees-0 degrees, the gradient direction of the pixel point P is theta, and obtaining the gradient linear interpolation G of the pixel points P1 and P2P1And Gp2The gradient intensity G of the current pixel is compared with the pixel G along the positive and negative gradient directionsP1、Gp2Making a comparison to suppress less than pixel GP1、Gp2Will be larger than the pixel GP1、Gp2The pixel points of (2) are used as edge points;
the process of obtaining the total number of sparks in the step 6 is as follows: connecting the eight connected regions, traversing image statistics to obtain 1 spark point, then expanding the eight neighborhoods of the spark points to search the regions, counting +1 when the searched regions are not expanded, continuously searching a next new point, and repeating the operation until the whole image is searched to obtain the total number of sparks.
The invention has the beneficial effects that: the method for identifying the plasma electrolytic oxidation reaction state based on image processing accurately detects the number of sparks generated on the surface of a sample at a certain moment in unit area through the image processing technology to identify the plasma electrolytic oxidation reaction state at the moment, and further accurately controls the reaction process according to the spark state evolution; the accuracy of identifying the reaction state of the plasma electrolytic oxidation is improved, and the reaction process can be accurately adjusted.
Drawings
FIG. 1 is a flow chart of a method of image processing based plasma electrolytic oxidation reaction state identification of the present invention;
FIG. 2 is an image of a spark to be processed in the method for identifying the state of a plasma electrolytic oxidation reaction based on image processing according to the present invention;
FIG. 3 is an image of a target spark in the method of image processing based plasma electrolytic oxidation reaction state identification of the present invention;
FIG. 4 is an image of target sparks separated from each other in the method for image processing based recognition of the status of a plasma electrolytic oxidation reaction according to the present invention;
FIG. 5 is an edge feature of a spark in the method for identifying the status of a plasma electrolytic oxidation reaction based on image processing according to the present invention;
fig. 6 is a spark image in the method for identifying the state of the plasma electrolytic oxidation reaction based on image processing according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The method for identifying the state of the plasma electrolytic oxidation reaction based on image processing is shown in FIG. 1 and comprises the following steps:
step 1, photographing and imaging the surface of a workpiece by adopting high-resolution photographing equipment, and obtaining a spark image to be processed and the area S of the surface of the workpiece as shown in figure 2;
specifically, the color of a pixel point in a spark image to be processed is converted into gray scale through a weighted average value method, and the conversion formula is as follows:
I(x,y)＝0.3*I_R(x,y)+0.59*I_G(x,y)+0.11*I_B(x,y) (1)；
in the above equation, 0.3,0.59, and 0.11 are weighting coefficients adjusted according to the human brightness perception system. The conversion of an RGB color image into a grayscale image is achieved by eliminating the hue and saturation information of the image while preserving the brightness.
Step 3, segmenting sparks and backgrounds of the grayed spark images to be processed to obtain target spark images, as shown in fig. 3;
specifically, firstly, taking a gray value mean value M of a grayed spark image to be processed, arbitrarily selecting a gray value T to divide a gray histogram of the image into a front part A and a rear part B, wherein the mean values of the two parts become MA and MB, the proportion of the pixel number of the part A to the total pixel number is marked as PA, the proportion of the pixel number of the part B to the total pixel number is marked as PB, calculating the maximum between-class variance and using the maximum between-class variance as a gray threshold value T, and defining the maximum between-class variance as:
ICV＝PAα*(MA-M)2+PBα*(MB-M)2 (2)；
in the above formula, α is 0.8, and the threshold T — ICV is 200.
Then, the image is segmented into a binary image by using a gray threshold value T:
T＝T[x,y,p(x,y),f(x,y)] (3)；
in the above formula, x and y represent horizontal and vertical coordinates of a pixel, p (x and y) represents a local characteristic of the pixel, f (x and y) represents a gray value of the pixel, and the thresholded image is a binary image defined as:
the pixel indicated by 0 is the target spark and thus the target spark image is obtained.
specifically, in the plasma electrolytic oxidation process, the spark causes the problem of adhesion of the spark in the image due to the arc mapping, and the counting accuracy is affected. And carrying out corrosion operation on the target spark image, and then expanding to obtain mutually separated spark targets. The processing method has the effects of smoothing the target contour, disconnecting the narrow connecting part and removing the fine bulges, so that each target spark is in a mutually separated state, the counting error is effectively reduced, and the subsequent processing is convenient, as shown in fig. 4.
Step 5, extracting the edge characteristics of each spark target in the target spark image;
step 5.1, in order to reduce as much as possible the influence of noise present in the spark image on the spark edge detection result, it is necessary to filter out the noise to prevent false detection caused by the noise. To smooth the image, a gaussian filter is convolved with the image to reduce the apparent noise contribution on the edge detector. Specifically, a Gaussian filter is used for performing convolution on a target spark image to obtain a filtered pixel point e;
in this embodiment, a gaussian filter kernel of (2k +1) x (2k +1) is used, and the equation is:
in the above equation, i and j are rows and columns of a gaussian convolution kernel matrix, σ is a variance, k determines the dimension of the kernel matrix, and a gaussian convolution kernel with σ of 1.4 and a size of 3 × 3(k of 1) is taken as:
setting a window of 3x3 in the target spark image as a, and a pixel point to be filtered as e, after gaussian filtering, the brightness value of the pixel point e is:
where, is the convolution sign, sum represents the sum of all elements in the matrix, h is the coefficient of the gaussian convolution kernel matrix, and a-i is the coefficient of window a.
Step 5.2, applying a pair of convolution arrays Sx,SyConvolving a window of 3x3 in the target spark image by using A and Sobel operators, and convolving an array Sx,SyComprises the following steps:
in the above formula, SxSobel operator representing the x-direction for detecting edges in the y-direction, SyA Sobel operator representing the y direction, which is used for detecting the edge of the x direction;
obtaining gradient values G of pixel points e in x and y directions after convolution respectivelyXAnd Gy：
In the above formula, is a convolution symbol, and sum represents the sum of all elements in the matrix;
then calculating the gradient strength G and the gradient direction theta of the pixel point e;
step 5.3, according to the gradient direction, carrying out non-maximum suppression on the gradient amplitude to obtain edge points;
specifically, the gradient directions are divided into E, NE, N, NW, W, SW, S and SE, wherein 0 represents 0-45 degrees, 1 represents 45-90 degrees, 2 represents-90 degrees-45 degrees, 3 represents-45 degrees-0 degrees, the gradient direction of the pixel point P is theta, and gradient linear interpolation G of the pixel points P1 and P2 is obtainedP1And Gp2，
tanθ＝Gy/Gx (14)；
GP1＝(1-tan(θ))*E+tan(θ)*NE (15)；
GP2＝(1-tan(θ))*W+tan(θ)*SW (16)；
The gradient intensity G of the current pixel and the pixel G in the positive and negative gradient directions are comparedP1、Gp2Making a comparison to suppress less than pixel GP1、Gp2Will be larger than the pixel GP1、Gp2The pixel points of (2) are used as edge points;
and 5.4, processing the edge points by using high and low thresholds to obtain the edge characteristics of each spark target. Specifically, edge pixels are filtered with a weak gradient strength and edge pixels with a high gradient strength are retained to account for the spurious response, here taking a high-to-low threshold ratio of 2:1, to obtain the edge characteristics of each spark target, as shown in FIG. 5.
And 6, communicating the edge characteristics by adopting a communication domain method to obtain the total number of sparks, dividing the total number by the surface area S of the workpiece to obtain the number of sparks in unit area, and identifying the plasma electrolytic oxidation reaction state at the current moment according to the number of sparks in unit area, so that the evolution state of the plasma electrolytic oxidation is accurately known, and a basis is provided for further accurately controlling the reaction process.
Specifically, the value after the binarization processing is only two values of 0 and 1, and the spark edge is clearly shown. The pixel denoted by 0 represents the spark edge that needs to be counted, while the adjacent pixel denoted by 0 assumes a connected state. Connecting eight connected regions of the pixel, which are represented by 0, of the upper region, the lower region, the left region, the upper right region, the lower left region and the lower right region, traversing image statistics to obtain 1 spark point, then expanding the eight neighborhood of the spark point to search a region, when the searched region is not expanded, indicating that the region is completely searched, counting +1, continuously searching a next new point, and repeating the operation until the whole image is searched, as shown in fig. 6, obtaining the total number of sparks.
Through the mode, the method for identifying the plasma electrolytic oxidation reaction state based on the image processing accurately detects the number of sparks generated on the surface of a sample at a certain moment in a unit area through the image processing technology to identify the reaction state of the plasma electrolytic oxidation at the moment, and further accurately controls the reaction process according to the spark state evolution; the accuracy of identifying the reaction state of the plasma electrolytic oxidation is improved, and the reaction process can be accurately adjusted.
Claims (7)
1. The method for identifying the plasma electrolytic oxidation reaction state based on image processing is characterized by comprising the following steps of:
step 1, acquiring a spark image to be processed, and obtaining the area S of the surface of a workpiece;
step 2, graying the spark image to be processed;
step 3, segmenting sparks and backgrounds of the grayed spark images to be processed to obtain target spark images;
step 4, performing morphological processing on the target spark image to obtain mutually separated spark targets;
step 5, extracting the edge characteristics of each spark target in the target spark image;
and 6, communicating the edge characteristics by adopting a communication domain method to obtain the total number of sparks, dividing the total number by the surface area S of the workpiece to obtain the number of sparks in unit area, and identifying the plasma electrolytic oxidation reaction state at the current moment according to the number of sparks in unit area.
2. The method for identifying the electrolytic oxidation reaction state of the plasma based on the image processing as claimed in claim 1, wherein the step 2 comprises the following specific processes: and converting the color of a pixel point in the spark image to be processed into gray scale by a weighted average value method.
3. The method for identifying the electrolytic oxidation reaction state of the plasma based on the image processing as claimed in claim 1, wherein the specific process of the step 3 is as follows: firstly, taking a gray value mean value M of a grayed spark image to be processed, arbitrarily selecting a gray value T to divide a gray histogram of the image into a front part A and a rear part B, wherein the mean value of the two parts is MA and MB, the proportion of the pixel number of the part A to the total pixel number is marked as PA, the proportion of the pixel number of the part B to the total pixel number is marked as PB, calculating the maximum between-class variance and taking the maximum between-class variance as a gray threshold value T, and then dividing the image into a binary image by using the gray threshold value T to obtain a target spark image.
4. The method for identifying the electrolytic oxidation reaction state of the plasma based on the image processing as claimed in claim 1, wherein the specific process of the step 4 is as follows: and carrying out corrosion operation on the target spark image, and then expanding to obtain mutually separated spark targets.
5. The method for identifying the electrolytic oxidation reaction state of the plasma based on the image processing as claimed in claim 1, wherein the step 5 comprises the following steps:
step 5.1, performing convolution on the target spark image by using a Gaussian filter to obtain a pixel point e after filtering;
step 5.2, applying a pair of convolution arrays Sx,SyConvolving a window of 3x3 in the target spark image as A and Sobel operators to obtain gradient values G of pixel points e in x and y directions respectivelyXAnd GyThen calculating the gradient strength G and the gradient direction theta of the pixel point e;
step 5.3, according to the gradient direction, carrying out non-maximum suppression on the gradient amplitude to obtain edge points;
and 5.4, processing the edge points by using a high-low threshold value to obtain the edge characteristics of each spark target.
6. The method for identifying the electrolytic oxidation reaction state of the plasma based on the image processing as claimed in claim 5, wherein the specific process of the step 5.3 is as follows: dividing the gradient direction into E, NE, N, NW, W, SW, S and SE, wherein 0 represents 0-45 degrees, 1 represents 45-90 degrees, 2 represents-90 degrees-45 degrees, 3 represents-45 degrees-0 degrees, the gradient direction of the pixel point P is theta, and obtaining the gradient linear interpolation G of the pixel points P1 and P2P1And Gp2The gradient intensity G of the current pixel is compared with the pixel G along the positive and negative gradient directionsP1、Gp2Making a comparison to suppress signals smaller than the pixel GP1、Gp2Will be larger than the pixel GP1、Gp2The pixel points of (2) are used as edge points.
7. The method for identifying the electrolytic oxidation reaction state of the plasma based on the image processing as claimed in claim 1, wherein the process of obtaining the total number of sparks in the step 6 is as follows: connecting the eight connected regions, traversing image statistics to obtain 1 spark point, then expanding the eight neighborhoods of the spark points to search the regions, counting +1 when the searched regions are not expanded, continuously searching a next new point, and repeating the operation until the whole image is searched to obtain the total number of sparks.
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| CN116958139B (en) * | 2023-09-20 | 2023-11-21 | 深圳市盘古环保科技有限公司 | Advanced oxidation intelligent monitoring method |
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