CN113484329A - Injection molding part defect detection method and system based on machine vision - Google Patents
Injection molding part defect detection method and system based on machine vision Download PDFInfo
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- 238000001746 injection moulding Methods 0.000 title claims abstract description 46
- 230000007547 defect Effects 0.000 title claims abstract description 16
- 238000001514 detection method Methods 0.000 title claims abstract description 12
- 238000000034 method Methods 0.000 claims abstract description 22
- 238000003466 welding Methods 0.000 claims abstract description 20
- 238000003708 edge detection Methods 0.000 claims description 12
- 238000002347 injection Methods 0.000 description 8
- 239000007924 injection Substances 0.000 description 8
- 230000004927 fusion Effects 0.000 description 3
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- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000155 melt Substances 0.000 description 2
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G06T5/00—Image enhancement or restoration
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G06T7/0004—Industrial image inspection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/10—Segmentation; Edge detection
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
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Abstract
The invention relates to the technical field of image processing, in particular to a method and a system for detecting defects of an injection molding part based on machine vision, wherein the method comprises the following steps: acquiring an injection molding image, and acquiring the edge of a hole and the lines of suspected welding marks in the injection molding image; for each suspected line of the weld mark, judging whether the line is the weld mark or not by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole; the smaller the deviation value is, the more the line points to the center point of the hole. The invention can effectively distinguish the weld mark and the scratch, so that the detection result only comprises the weld mark, and the detection precision of the weld mark is improved.
Description
Technical Field
The invention relates to the field of image processing, in particular to a method and a system for detecting defects of an injection molding part based on machine vision.
Background
Weld marks are common product defects in the injection molding industry, influence can be caused not only to the appearance of a product, but also to the strength of the product, and the two influences can greatly reduce the product value of an injection molding part. In the prior art, a method for detecting the weld mark is generally a conventional image processing technology such as edge detection, all abnormal lines on the surface of the injection molding part are detected, the weld mark can be detected, and some defects different from the weld mark, such as surface scratch of the injection molding part, can also be detected. The influence of mar and weld mark to the injection molding quality is different: the scratch can affect the aesthetic property, but can not affect the use, and the defects of injection molding parts which do not pay attention to the appearance are not calculated; the weld marks not only can affect the appearance, but also can cause the injection molding piece to be easy to break at the weld marks, and the strength of the injection molding piece is affected.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a machine vision-based injection molding part defect detection method and system, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a machine vision-based injection molding defect detection method, which includes the following specific steps:
acquiring an injection molding image, and acquiring the edge of a hole and the lines of suspected welding marks in the injection molding image;
for each suspected line of the weld mark, judging whether the line is the weld mark or not by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole; calculating the distance between the line and the hole based on the end point of the line; the method for acquiring the deviation value of one line pointing to the center point of one hole specifically comprises the following steps: determining a reference point, wherein the reference point is the end point with smaller distance from the hole in the two end points of the line; calculating the slope average value of the slope of the connection line of each point and the reference point on the line, generating a straight line with the slope as the slope average value after passing through the reference point, and representing the deviation value of the line pointing to the center point of the hole by the distance from the center point of the hole to the straight line; the smaller the deviation value, the more the line points to the hole center point.
Further, for a line and a hole, the smaller the distance from the center point of the hole to the line corresponding to the line, the smaller the deviation value of the line from the center point of the hole, which indicates that the line is more likely to be a weld mark caused by the hole.
Furthermore, after edge detection is carried out on the injection molding image, Hough transformation is carried out, and the edges of the holes and lines of suspected weld marks are obtained.
In a second aspect, another embodiment of the present invention provides a machine vision-based system for detecting defects in injection molded parts, the system specifically comprising:
the image processing module is used for acquiring an injection molding image and acquiring the edges of the holes and lines of the suspected welding marks in the injection molding image;
the welding mark judging module is used for judging whether each line of each suspected welding mark is a welding mark or not by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole; calculating the distance between the line and the hole based on the end point of the line; the method for acquiring the deviation value of one line pointing to the center point of one hole specifically comprises the following steps: determining a reference point, wherein the reference point is the end point with smaller distance from the hole in the two end points of the line; calculating the slope average value of the slope of the connection line of each point and the reference point on the line, generating a straight line with the slope as the slope average value after passing through the reference point, and representing the deviation value of the line pointing to the center point of the hole by the distance from the center point of the hole to the straight line; the smaller the deviation value, the more the line points to the hole center point.
Further, for a line and a hole, the smaller the distance from the center point of the hole to the line corresponding to the line, the smaller the deviation value of the line from the center point of the hole, which indicates that the line is more likely to be a weld mark caused by the hole.
Furthermore, after edge detection is carried out on the injection molding image, Hough transformation is carried out, and the edges of the holes and lines of suspected weld marks are obtained.
The embodiment of the invention at least has the following beneficial effects: for each suspected line of the weld mark, judging whether the line is the weld mark or not by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole; therefore, the method can effectively distinguish the weld mark and the scratch, so that the detection result only comprises the weld mark, the detection precision of the weld mark is improved, and the method is favorable for evaluating the quality of a subsequent injection molding part.
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 an embodiment of the method of the present invention.
FIG. 2 is a graph showing the results of edge detection of an image of an injection molded part according to an embodiment of the present invention.
Fig. 3 is an image obtained by performing hough transform on the edge detection result graph in the embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given of a method and a system for detecting defects of injection molding parts based on machine vision according to the present invention, with reference to the accompanying drawings and preferred embodiments, and the detailed description thereof will be provided below. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily 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.
Weld marks are produced during the production of injection molded parts by incomplete fusion of the melt due to improper temperature control of the melt, or by incomplete fusion of the molten plastic streams as they merge in multiple strands in the mold cavity due to the presence of inserts or holes. Because the weld marks are caused by incomplete fusion of the molten materials, the injection molding part is easy to generate stress concentration at the weld marks, fracture is generated, and the strength of the injection molding part is further influenced. The invention further aims to process the surface image of the injection molding part by using a machine vision technology and detect the welding mark on the surface of the injection molding part.
The following application scenarios are taken as examples to illustrate the present invention:
the application scene is as follows: the method comprises the steps of setting a camera on a quality inspection production line of injection molded parts, collecting images of the surfaces of the injection molded parts with holes, processing the collected images, and detecting whether the surfaces of the injection molded parts have weld mark defects or not.
Referring to fig. 1, a flowchart illustrating an implementation of a machine vision-based injection molding defect detection method according to an embodiment of the present invention is shown, the method including the following steps:
and step S1, acquiring an injection molding image, and acquiring the edges of the holes and lines of the suspected welding marks in the injection molding image.
Carrying out Hough transformation after edge detection is carried out on an injection molding image of a certain plastic injection molding to obtain the edge of a hole and the lines of suspected weld marks; specifically, after converting the acquired RGB image of the injection molded part to be detected into a gray scale image, performing edge detection on the image, as shown in fig. 2, to obtain an edge detection result image of the injection molded part image; preferably, edge detection is performed using the canny operator; and (4) carrying out Hough transform on the edge detection result graph, as shown in fig. 3, to obtain the edge of the hole and the lines of the suspected weld marks.
Step S2, for each suspected weld mark line, determining whether the line is a weld mark by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole; the smaller the deviation value is, the more the line points to the center point of the hole.
According to the principle of weld mark formation in the injection molding process, the weld mark is often present near the hole, and the weld mark diverges outward along the center of the hole. Therefore, it is necessary to determine whether the line of the suspected weld mark is a weld mark from the specific distribution relationship between the weld mark and the hole.
(a) And calculating the distance between each line of the suspected weld marks and each hole.
Calculating the distance between the lines and the holes based on the end points of the lines, in particular, two end points per line, for line LpCalculating the line LpRespectively to the hole Q1Selecting the minimum value d of the distance of each edge point on the edgep1Then calculate the line LpTwo end points ofRespectively to the hole Q2Selecting the minimum value d of the distance of each edge point on the edgep2By analogy, the line L is obtainedpThe minimum distance between the two end points and each hole is obtained as dp1，dp2，……，dpkK, the total number of the minimum distances, k represents k holes; wherein the distance is a Euclidean distance.
Based on lines LpThe minimum distance between the two end points and each hole, and the line L is calculatedpProximity to each hole, by hole Q1For example, the line L is explainedpNear hole Q1Closeness dT p 1: dT p1= dp/dp1Wherein d isp=dp1+dp2+，……，+dpkMinimum value dp2The smaller, the line L is illustratedpCloser to the hole Q1The greater the value of the corresponding closeness dT p 1; by the same token, the line L can be obtainedpThe closeness between the holes and the holes is dT p1, dT p2, dT p3, … … and dT pk respectively; for better judgment of whether the line is a weld mark, the k closeness degrees are normalized, specifically: dpi=dT pi/(dT p1+dT p2+dT p3+……+dT pk)，DpiLine of expression LpThe value range of i is [1, k ] as a result of the normalization of the closeness dT pi between the hole and the ith hole]。
(b) Based on the formation process of the weld mark, the weld mark starts to extend outwards from the edge of the hole along the radius direction of the hole and finally stops; therefore, for each line of the suspected weld mark, the deviation value of the line pointing to the center point of each hole is calculated.
Specifically, the obtaining of the deviation value of one line pointing to the center point of one hole specifically comprises:
determining a reference point, wherein the reference point is the end point with smaller distance from the hole in the two end points of the line; calculating the slope average value of the slope of the connection line between each point and the reference point on the line, passing the reference point, generating a straight line with the slope as the slope average value, wherein the generated straight line is a straight line corresponding to the line, and the characterization line of the distance from the hole center point to the straight line points to the hole center pointDeviation value z ofp(ii) a The smaller the deviation value, the closer the extending direction of the line is to a certain radius direction. For the line LpThe line L can be obtained according to the above methodpDeviation values z pointing to the center points of k holes respectivelyp1，zp2，……，zpk。
For the line LpCalculating for line L based on k deviation valuespDegree of deviation pointing to center points of k holes, respectively, zT p1= zp/zpiWherein z isp=zp1+zp2+，……，+zpkAnd zT p1 denotes a line LpDeviation degree from the center point of the ith hole, deviation value zpiThe smaller, the line L is illustratedpThe more the point is to the central point of the ith hole, the larger the corresponding value of the deviation degree zT p1 is, and the value range of i is [1, k ]](ii) a Similarly, the k deviation degrees are normalized, specifically: zpi=zT pi/(zT p1+zT p2+zT p3+……+zT pk)，ZpiLine of expression LpThe degree of deviation zT p1 from the center point of the ith hole was normalized.
(c) For the line LpAnd judging whether the line is a welding mark or not by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole. For a line and a hole, the smaller the distance from the center point of the hole to the straight line corresponding to the line, the smaller the deviation value of the line from the center point of the hole, which indicates that the line is more likely to be a weld mark caused by the hole.
Preferably, for the line LpAccording to line LpThe proximity between the holes and the lines LpDeviation degree judging line L respectively pointing to k hole center pointspWhether the welding mark is a welding mark or not; in particular, according to the line LpProximity D to ith holepiAnd lines LpDegree of deviation Z from the center point of the ith holepiCalculating line LpDegree of matching M with ith holepi：
Further, for the line LpLine L can be obtainedpRespectively matching with k holes, and calculating a line L based on the minimum value of the matching degree and the minimum value of the matching degree in the k matching degreespProbability of weld mark phipIn particular,. phip=max{Mp1,Mp2,……,Mpk}-min{Mp1,Mp2,……,MpkMean time probability phipLine L is greater than the predetermined probability thresholdpThe weld mark is formed.
If the injection-molded part image includes W lines, each line corresponds to a probability Φ, X = ∑ W p =1 ΦpAnd when the X is more than or equal to the number k of the holes, the injection molding piece is unqualified, otherwise, the injection molding piece is qualified.
Based on the same concept as the method embodiment, the invention provides a machine vision-based injection molding part defect detection system, which comprises the following steps:
the image processing module is used for acquiring an injection molding image and acquiring the edges of the holes and lines of the suspected welding marks in the injection molding image;
the welding mark judging module is used for judging whether each line of each suspected welding mark is a welding mark or not by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole; calculating the distance between the line and the hole based on the end point of the line; the method for acquiring the deviation value of one line pointing to the center point of one hole specifically comprises the following steps: determining a reference point, wherein the reference point is the end point with smaller distance from the hole in the two end points of the line; calculating the slope average value of the slope of the connection line of each point and the reference point on the line, generating a straight line with the slope as the slope average value after passing through the reference point, and representing the deviation value of the line pointing to the center point of the hole by the distance from the center point of the hole to the straight line; the smaller the deviation value, the more the line points to the hole center point.
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 is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A machine vision-based injection molding part defect detection method is characterized by comprising the following steps:
acquiring an injection molding image, and acquiring the edge of a hole and the lines of suspected welding marks in the injection molding image;
for each suspected line of the weld mark, judging whether the line is the weld mark or not by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole; calculating the distance between the line and the hole based on the end point of the line; the method for acquiring the deviation value of one line pointing to the center point of one hole specifically comprises the following steps: determining a reference point, wherein the reference point is the end point with smaller distance from the hole in the two end points of the line; calculating the slope average value of the slope of the connection line of each point and the reference point on the line, generating a straight line with the slope as the slope average value after passing through the reference point, and representing the deviation value of the line pointing to the center point of the hole by the distance from the center point of the hole to the straight line; the smaller the deviation value, the more the line points to the hole center point.
2. The method of claim 1, wherein for a line and a hole, the smaller the distance from the center point of the hole to the line corresponding to the line, the smaller the deviation of the line from the center point of the hole, indicating that the line is more likely to be a weld mark due to the hole.
3. The method of claim 1, wherein the injection molding image is subjected to hough transform after edge detection to obtain the edges of the holes and the lines of suspected weld marks.
4. A machine vision based injection molding defect detection system, comprising:
the image processing module is used for acquiring an injection molding image and acquiring the edges of the holes and lines of the suspected welding marks in the injection molding image;
the welding mark judging module is used for judging whether each line of each suspected welding mark is a welding mark or not by combining the distance between the line and each hole and the deviation value of the line pointing to the center point of each hole; calculating the distance between the line and the hole based on the end point of the line; the method for acquiring the deviation value of one line pointing to the center point of one hole specifically comprises the following steps: determining a reference point, wherein the reference point is the end point with smaller distance from the hole in the two end points of the line; calculating the slope average value of the slope of the connection line of each point and the reference point on the line, generating a straight line with the slope as the slope average value after passing through the reference point, and representing the deviation value of the line pointing to the center point of the hole by the distance from the center point of the hole to the straight line; the smaller the deviation value, the more the line points to the hole center point.
5. The system of claim 4, wherein for a line and a hole, the smaller the distance from the center point of the hole to the line corresponding to the line, the smaller the deviation of the line from the center point of the hole, indicating that the line is more likely to be a weld mark due to the hole.
The system of claim 4, wherein the injection-molded part image is subjected to hough transform after edge detection to obtain the edges of the holes and the lines of suspected weld marks.
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| CN113936000A (en) * | 2021-12-16 | 2022-01-14 | 武汉欧易塑胶包装有限公司 | Injection molding wave flow mark identification method based on image processing |
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| 毛霆: "塑料注射成形产品质量智能检测技术研究", 《中国博士学位论文全文数据库 工程科技Ⅰ辑》 * |
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| CN113936000A (en) * | 2021-12-16 | 2022-01-14 | 武汉欧易塑胶包装有限公司 | Injection molding wave flow mark identification method based on image processing |
| CN113936000B (en) * | 2021-12-16 | 2022-03-15 | 武汉欧易塑胶包装有限公司 | Injection molding wave flow mark identification method based on image processing |
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