CN114758185A - Injection molding parameter control method and system based on gray level chromatic aberration - Google Patents
Injection molding parameter control method and system based on gray level chromatic aberration Download PDFInfo
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- 239000002932 luster Substances 0.000 description 2
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Abstract
The invention relates to the technical field of image processing, in particular to an injection molding parameter control method and system based on gray level chromatic aberration, wherein the method comprises the following steps: graying the collected surface image of the plastic product, and drawing a grayscale histogram according to the grayed image information; fitting the data of the gray level histogram to obtain a gray level curve, and acquiring all maximum value points on the gray level curve to form a maximum value point set; determining a clustering radius by the longitudinal coordinate value of the maximum value point, respectively taking the maximum value point on the gray curve as the circle center, clustering the pixel points according to the gray histogram to obtain a category set, wherein the category set is a classification result; and analyzing the defects of the plastic products according to the classification result. The invention reduces the manpower loss and improves the accuracy of detecting the color defects.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an injection molding parameter control method and system based on gray level chromatic aberration.
Background
Injection molding is one of the main methods for plastic processing, and plays an important role in the production of plastic products, and with the progress of modern technology, polymer materials are used in various fields, and the requirements for plastic products are continuously increased. The injection molding machine is a machine for producing injection molding parts, is frequently used in the current society, and may be needed by various industries for injection molding, but in the injection molding process, the color defects of the produced products caused by the reasons of temperature, stirring speed and the like are inevitable. The traditional method is manual detection, and the corresponding defects are processed after detection, so that the labor cost is increased, and the automatic target is not met.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a method and a system for controlling injection molding parameters based on gray-scale color difference, wherein the adopted technical scheme is as follows:
graying the collected surface image of the plastic product, and drawing a grayscale histogram according to the grayed image information;
fitting the data of the gray level histogram to obtain a gray level curve, and acquiring all maximum value points on the gray level curve to form a maximum value point set;
determining the clustering radius according to the longitudinal coordinate value of the maximum value point, and calculating the area density corresponding to the maximum value point by respectively taking the maximum value point on the gray scale curve as the circle center;
on the gray curve, respectively taking the maximum value point as a first initial point, calculating the area density corresponding to two adjacent coordinate points, taking the coordinate point with high area density as a second initial point, and stopping until the area density corresponding to the current coordinate point is maximum;
acquiring maximum value points contained in all final stop time areas, and recording corresponding abscissas as a category set, wherein the category set is a classification result;
and analyzing the defects of the plastic products according to the classification result.
Preferably, the method for acquiring the minimum value point set further includes: and fitting the data of the gray level histogram to obtain a gray level curve, wherein the abscissa of a coordinate system where the gray level curve is located is gray level, and the ordinate is the number of pixel points, and all the maximum value point sets and minimum value point sets on the gray level curve are obtained.
Preferably, the method further comprises the step of correcting the classification result, and the specific steps are as follows:
on a gray curve, a maximum value point corresponds to two adjacent minimum value points in a minimum value point set, the sum of longitudinal coordinate values corresponding to coordinate points between the two adjacent minimum value points is calculated to obtain the total number of pixel points, and the total number of all the pixel points forms a pixel point number set; and calculating the average value of elements in the pixel point number set, acquiring the total number of the pixel points larger than the average value, and recording the abscissa of the maximum value point between the two corresponding two-stage small value points into the classification result.
Preferably, the density obtaining method specifically includes:
wherein,
the density of the area in the circle, p and q are the gray levels corresponding to the intersection points of the circle and the gray curve respectively,
to be the radius of the cluster, the cluster radius,
the number of pixels having a gray level i is represented.
The invention also provides an injection molding parameter control system based on gray-scale color difference, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the computer program realizes the steps of the injection molding parameter control method based on gray-scale color difference when being executed by the processor.
The embodiment of the invention at least has the following beneficial effects:
the invention classifies the pixel points on the gray level histogram by utilizing the maximum value to obtain the classification result, analyzes the defects of the plastic products according to the classification result, and can reduce the manpower loss by utilizing the image processing technology. Meanwhile, the invention also corrects the classification result with errors according to the minimum value, and regulates and controls the parameters of the defects according to the classification result, thereby improving the accuracy of detecting the color defects.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for controlling injection molding parameters based on gray scale and color difference.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following detailed description of the injection molding parameter control method and system based on gray-scale color difference according to the present invention with reference to the accompanying drawings and preferred embodiments, the detailed implementation, structure, features and effects thereof are described below. In the following description, the different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of an injection molding parameter control method and system based on gray scale and color difference in detail with reference to the accompanying drawings.
Example 1:
the invention is applicable to the following specific scenes: in the production process of the mobile phone shell, various defects can occur, wherein the problem of surface color defect can occur, and the problem of color depth or different color phases is reflected on the surface of a product. Wherein, the color has the depth as follows: under the influence of certain factors, the same color displays different darker and lighter colors, and the reaction shows different gray values on the gray map. In this case, the difference in the depth ratio is not large in the injection molding, and thus the case of a small ratio is not considered in the present invention. The different hues appear as: the plastic cement is produced by mixing plastic cement with other colors on the surface of the product, wherein the plastic cement is formed by mixing a mass of plastic cement and a lump of plastic cement.
Referring to fig. 1, a flowchart of steps of a method and a system for controlling injection molding parameters based on gray-scale color difference according to an embodiment of the present invention is shown, the method includes the following steps:
firstly, graying the collected surface image of the plastic product, and drawing a grayscale histogram according to the grayed image information.
In this embodiment, a camera is provided to capture surface images of the sample cell phone case, resulting in front and back images of the cell phone case, respectively. The collected front image of the mobile phone shell is grayed, in the embodiment, a weighted average method which best meets human eye observation is adopted for graying to obtain a gray level image, and a corresponding gray level histogram is obtained according to the number of pixel points and the gray level of the gray level image.
It should be noted that, when there is no problem in the sample, that is, when there is no defect in the production, the produced mobile phone shell has only two colors, namely, the background color and the main body color, and at this time, there are only two required maximum values corresponding to the histogram. The problem that colors have different shades or different hues may occur in production, then, the sample is subjected to image acquisition, if the two defects occur, 4 different colors are reflected on the image, and correspondingly, 4 maximum value peak values exist in a histogram of the image, so that the number of types of colors can be judged through the maximum values, and the defect can be judged according to the size of the maximum values.
And then, fitting the data of the gray level histogram to obtain a gray level curve, and respectively obtaining all maximum value points and minimum value points on the gray level curve to form a maximum value point set and a minimum value point set.
Specifically, curve fitting calculation is performed on the data of the gray level histogram to obtain a gray level curve, and the gray level curve is recorded as
. In a coordinate system where the gray curve is located, the abscissa represents gray level, and the ordinate represents the number of pixels.
And calculating according to the function of the gray curve to obtain all the maximum value point sets and minimum value point sets on the gray curve. In particular, for gray scale curves
Performing derivation calculation to obtain
Let us order
Obtaining the extreme point of the gray curve function if
And is and
at this time, the point corresponding to i is the maximum value point, and all the maximum value point sets are obtained and recorded as
Wherein r is the number of maximum points.
Similarly, if
And is and
at this time, i corresponds to a minimumValue points, obtaining all minimum value point sets, marking as
Wherein c is the number of minimum value points.
Then, determining a clustering radius according to the longitudinal coordinate value of the maximum value point, and calculating the area density corresponding to the maximum value point by respectively taking the maximum value point on the gray scale curve as the circle center; on the gray curve, respectively taking the maximum value point as a first initial point, calculating the area density corresponding to two adjacent coordinate points, taking the coordinate point with high area density as a second initial point, and stopping until the area density corresponding to the current coordinate point is maximum; and acquiring maximum value points contained in all the final stop time zones, recording the corresponding abscissa as a class set, wherein the class set is a classification result, and correcting the classification result.
It should be noted that, in this embodiment, the idea of the mean shift clustering algorithm is adopted to select the required maximum value, in this clustering method, the value of the radius needs to be determined first, and the effects generated by different clustering radii are different, that is: given a circle of radius R, as the dots move, the circle moves until the density within the circle reaches a maximum.
Therefore, in this embodiment, when the produced mobile phone shell has no defects, the mobile phone shell only contains the background color and the body color, and only two required maximum values are reflected on the gray level histogram at this time; when the produced mobile phone shell only contains one color defect, three required maximum values are reflected on the gray level histogram; when the handset housing is produced to contain two color defects, there are four maxima required as reflected in the gray level histogram. When the image is collected, the mobile phone shell and the background color in the collected image are controlled to respectively account for half, so that the abscissa of all the maximum values is substituted into the function of the gray curve, the maximum value in the maximum values is obtained and is marked as MAX, and the maximum value is expressed by a formula as follows:
wherein MAX is the maximum value,
is a set of maximum values.
In this embodiment, a half of the maximum MAX is obtained as the diameter of the circle in the clustering algorithm, and the obtained clustering radius is:
. It should be noted that the selection principle of the cluster radius is as follows: the circle with the maximum value point as the center of the circle can not intersect with other peaks, and an implementer can determine the value of the clustering radius according to the actual situation.
Specifically, on the gray scale curve, traversal cycles are respectively started with the maximum value point as a first initial point, and the density of the area in the circle is compared:
if the density of the area in the circle with the two points on the left and the right of the maximum value point as the circle center is less than the density of the area in the circle with the maximum value point as the circle center, recording the maximum value point.
If the density of the area in the circle taking the two points on the left and right of the maximum value point as the circle center is greater than the density of the area in the circle taking the maximum value point as the circle center, taking the coordinate point with the greater density of the area in the circle as a second starting point; calculating the density of the areas in the circle corresponding to the two adjacent coordinate points of the second starting point, taking the coordinate point with higher density of the areas in the circle as a third starting point, repeating the steps to obtain the q-th starting point, calculating the density of the areas in the circle corresponding to the two adjacent coordinate points of the q-th starting point, stopping until the density of the areas in the circle corresponding to the current coordinate point is maximum, obtaining the maximum value points contained in all the final stop time areas, and marking the corresponding horizontal coordinates as category sets
(ii) a The number of elements in the category set is a category number b, and the category set is a classification result; is formulated as:
wherein z is the abscissa of the first initial point,
the density of the area within the circle centered at z, and b the number of classes.
The density acquisition method specifically comprises the following steps:
wherein,
the density of the area in the circle, p and q are the gray levels corresponding to the intersection points of the circle and the gray curve respectively,
to be the radius of the cluster, the cluster radius,
the number of pixels having a gray level i is represented.
Calculating the category number b according to the method, wherein it should be noted that in this embodiment, the problem that the produced mobile phone shell may have two color defects is solved, so the category number b includes at most four, that is, the main body color, the background color and the two color defects of the injection molding part;
if the number of categories in the classification result is b =4, the sample includes two color defects at this time.
If the number of categories in the classification result is b < 4, the number of pixels occupied by color areas with different hues attached to the surface of the mobile phone shell is too small due to the fact that the charging barrel cannot be cleaned completely during material transferring, and then the classification number is reduced, and therefore other methods need to be adopted to correct the maximum value in the classification result.
The correction step is as follows: on a gray curve, a maximum value point corresponds to two adjacent minimum value points in a minimum value point set, the sum of longitudinal coordinate values corresponding to coordinate points between the two adjacent minimum value points is calculated to obtain the total number of pixel points, and the total number of all the pixel points forms a pixel point number set; and calculating the average value of elements in the pixel point number set, acquiring the total number of the pixel points larger than the average value, and recording the abscissa of the maximum value point between the two corresponding two-stage small value points into the classification result.
Specifically, the ordinate values corresponding to the coordinate points between two adjacent points in the minimum value set are accumulated, and are expressed by a formula:
wherein m represents the total number of pixel points corresponding to two adjacent minimum value intervals,
as a function of the gray-scale curve,
、
respectively, an nth minimum value and an mth minimum value in the minimum value set, representing two minimum values adjacent on the gray scale curve, an
. Simultaneously in the two adjacent minimum value intervals
Corresponding to a maximum point.
According to the method, the total number of the pixel points between all the adjacent two minimum value points in the minimum value set is calculated to form a pixel point number set, namely the pixel point number set
Where k is the number of elements in the set of pixel numbers. Arranging elements in the pixel number set in a descending order, removing the total number of the first b pixels according to the category number b, and calculating the average value in the residual pixel number
Is formulated as:
wherein,
the number of the v-th element in the pixel number set is k, the number of the elements in the pixel number set is k, and the number of the categories is b.
It should be noted that, a peak exists in the gray scale curve due to an error or other reasons, the number of the pixels occupied by the part of gray scales is small, if the number of the categories is reduced due to the clustering error, that is, a part of colors is not detected, and if an undetected color exists, the number of the pixels occupied by the gray scale corresponding to the color is far greater than the number of the pixels occupied by other regions due to the error. So that the total number of acquired pixels is greater than the mean (i.e. the
) The abscissa of the maximum value point corresponding to the interval is recorded into the classification result, namely the classification set, so as to obtain a corrected classification set
Wherein x is a correction class setThe number of the elements in the Chinese character.
And finally, analyzing the sample defects according to the number of the classes in the corrected class set, and adjusting the parameters of the injection molding machine.
Specifically, according to the number of categories in the correction category set (denoted as correction category number x), how many different colors exist in the surface image of the mobile phone shell can be obtained, and the defects of the mobile phone shell are analyzed:
1) when the number of correction categories is x =4, it is indicated that the sample includes two color defects, and the two color defects need to be controlled and eliminated respectively. The two color defects are: the defects of uneven color and luster caused by different color depths and the defects of other colors on the surface of the mobile phone shell caused by unclean cleaning of the charging barrel during material transferring are overcome. Therefore, the stirring speed needs to be increased, the color master is uniformly distributed, and the alarm indicates that the last injection molding material remains and the charging barrel needs to be cleaned.
2) When the number of correction categories is x < 4, two cases are distinguished:
when the number of correction classes is x =2, the sample is free of defects.
When the number of correction categories is x =3, it is described that the sample has only one color defect, but it is not determined which defect is the case, and therefore, the maximum value points in the correction category set are compared, and the correction category set at this time is described as the correction category set
Sorting the values of the maximum value points in the correction category set from large to small, and recording as:
when in use
Meanwhile, the defect of uneven color and luster caused by different color shades is overcome; if it is
In time, the charging barrel cannot be cleaned completely during material transferring, so that the surface of the mobile phone shell has the defect of other colors. Wherein, 0.3 is a set threshold value, and the implementer can select according to the actual situation.
It should be noted that, for two color defects, if the color is not uniform due to different color depths, the proportion of the pixel points is larger; if the cell-phone shell surface that causes because the feed cylinder washs unclean when changeing the material has the defect of other colours, the proportion that its pixel accounts for is less.
According to the method, which color defect is contained in the sample when the number of correction categories is x =3 can be judged, and corresponding adjustment measures are taken for the color defect. Specifically, when
In the process, the stirring speed needs to be increased, so that the color master is uniformly distributed. When in use
In the process, the material residue of the last injection molding is prompted by an alarm, and then the charging barrel is cleaned.
When the color defect problem occurs in sample detection, the color defect detection method is adjusted according to the method until the final class number is 2, and at the moment, a product without the defect can be obtained, namely, the parameter regulation and control of the defect is realized. And simultaneously, carrying out the same defect detection treatment on the reverse side of the product according to the steps to finally obtain the product with uniform color.
Example 2:
the embodiment provides an injection molding parameter control system based on gray-scale color difference, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the injection molding parameter control method based on gray-scale color difference when being executed by the processor. Since embodiment 1 has already described a detailed description of the injection molding parameter control method based on gray scale color difference, it will not be described too much here.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (5)
1. An injection molding parameter control method based on gray level color difference is characterized by comprising the following steps:
graying the collected surface image of the plastic product, and drawing a grayscale histogram according to the grayed image information;
fitting the data of the gray level histogram to obtain a gray level curve, and acquiring all maximum value points on the gray level curve to form a maximum value point set;
determining a clustering radius according to the longitudinal coordinate value of the maximum value point, and calculating the area density corresponding to the maximum value point by respectively taking the maximum value point on the gray scale curve as the circle center;
on the gray curve, respectively taking the maximum value point as a first initial point, calculating the area density corresponding to two adjacent coordinate points, taking the coordinate point with high area density as a second initial point, and stopping until the area density corresponding to the current coordinate point is maximum;
acquiring maximum value points contained in all final stop time areas, and recording corresponding abscissas as a category set, wherein the category set is a classification result;
the method also comprises the step of correcting the classification result, and the specific steps are as follows:
on a gray curve, a maximum value point corresponds to two adjacent minimum value points in a minimum value point set, the sum of longitudinal coordinate values corresponding to coordinate points between the two adjacent minimum value points is calculated to obtain the total number of pixel points, and the total number of all the pixel points forms a pixel point number set;
calculating the average value of elements in the pixel number set, acquiring the total number of pixels larger than the average value, and recording the abscissa of the maximum value point between two corresponding small value points of two poles into the classification result;
and analyzing the defects of the plastic products according to the classification result.
2. The method for controlling injection molding parameters based on gray-scale color difference as claimed in claim 1, wherein the method for obtaining the minimum value point set further comprises:
and fitting the data of the gray histogram to obtain a gray curve, wherein the abscissa of a coordinate system where the gray curve is located is gray level, and the ordinate is the number of pixel points, and all the maximum value point sets and minimum value point sets on the gray curve are obtained.
3. The injection molding parameter control method based on gray level chromatic aberration as claimed in claim 1, wherein the method for obtaining the inner circle density specifically comprises:
wherein,
the density of the area in the circle, p and q are the gray levels corresponding to the intersection points of the circle and the gray curve respectively,
to be the radius of the cluster, the cluster radius,
the number of pixels having a gray level i is represented.
4. The injection molding parameter control method based on gray-scale color difference as claimed in claim 1, wherein said analyzing the plastic product defects according to the classification result specifically comprises:
the classification result at most comprises the main body color, the background color and two color defects of the injection molding;
if the number of categories in the classification result is 4, the color defect comprises two color defects;
if the number of categories in the classification result is 3, only one color defect is contained at the time;
if the number of classes in the classification result is 2, the injection molding part has no defects at this time.
5. An injection molding parameter control system based on gray-scale color difference, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the injection molding parameter control method based on gray-scale color difference as claimed in any one of claims 1 to 4.
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| CN115082482A (en) * | 2022-08-23 | 2022-09-20 | 山东优奭趸泵业科技有限公司 | Metal surface defect detection method |
| CN115222732A (en) * | 2022-09-15 | 2022-10-21 | 惠民县黄河先进技术研究院 | Injection molding process anomaly detection method based on big data analysis and color difference detection |
| CN115311375A (en) * | 2022-10-10 | 2022-11-08 | 南通安昇纺织品有限公司 | Compression storage and transmission method and system for data of check fabric |
| CN115609874A (en) * | 2022-10-09 | 2023-01-17 | 广州中誉精密模具有限公司 | Intelligent production method of automobile bumper |
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| CN115082482B (en) * | 2022-08-23 | 2022-11-22 | 山东优奭趸泵业科技有限公司 | Metal surface defect detection method |
| CN115222732A (en) * | 2022-09-15 | 2022-10-21 | 惠民县黄河先进技术研究院 | Injection molding process anomaly detection method based on big data analysis and color difference detection |
| CN115609874A (en) * | 2022-10-09 | 2023-01-17 | 广州中誉精密模具有限公司 | Intelligent production method of automobile bumper |
| CN115311375A (en) * | 2022-10-10 | 2022-11-08 | 南通安昇纺织品有限公司 | Compression storage and transmission method and system for data of check fabric |
| CN118015005A (en) * | 2024-04-10 | 2024-05-10 | 合肥工业大学 | Machine vision-based whiskering detection method and portable detection device |
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